{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Demonstration of mermaid's simple registration interface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Will read from /Users/rkwitt/Remote/mermaid/mermaid/../settings/compute_settings.json\n",
      "Will read from /Users/rkwitt/Remote/mermaid/mermaid/../settings/compute_settings_comments.json\n",
      "Will read from /Users/rkwitt/Remote/mermaid/mermaid/../settings/baseconf_settings.json\n",
      "Will read from /Users/rkwitt/Remote/mermaid/mermaid/../settings/baseconf_settings_comments.json\n",
      "Will read from /Users/rkwitt/Remote/mermaid/mermaid/../settings/algconf_settings.json\n",
      "Will read from /Users/rkwitt/Remote/mermaid/mermaid/../settings/algconf_settings_comments.json\n",
      "Will read from /Users/rkwitt/Remote/mermaid/mermaid/../settings/democonf_settings.json\n",
      "Will read from /Users/rkwitt/Remote/mermaid/mermaid/../settings/democonf_settings_comments.json\n",
      "Will read from /Users/rkwitt/Remote/mermaid/mermaid/../settings/datapro_settings.json\n",
      "Will read from /Users/rkwitt/Remote/mermaid/mermaid/../settings/datapro_settings_comments.json\n",
      "Will read from /Users/rkwitt/Remote/mermaid/mermaid/../settings/respro_settings.json\n",
      "Will read from /Users/rkwitt/Remote/mermaid/mermaid/../settings/respro_settings_comments.json\n",
      "WARNING: nn_interpolation could not be imported (only supported in CUDA at the moment). Some functionality may not be available.\n"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import mermaid.simple_interface as SI\n",
    "import mermaid.example_generation as EG\n",
    "import mermaid.module_parameters as pars"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we create an empty parameter dictionary ...."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = pars.ParameterDict()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we create a synthetic image pair in 2D to register. We will also be adding noise to the background of these images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating new category: root.square_example_images\n",
      "Using default value = 10 for key = len_s of category = root.square_example_images\n",
      "Using default value = 16 for key = len_l of category = root.square_example_images\n"
     ]
    }
   ],
   "source": [
    "use_synthetic_test_case = True\n",
    "add_noise_to_bg = True\n",
    "dim = 2\n",
    "sz = 64\n",
    "\n",
    "szEx = np.tile(sz, dim)\n",
    "I0_syn, I1_syn, spacing_syn = EG.CreateSquares(dim, add_noise_to_bg).create_image_pair(szEx, params) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`I0` and `I1` effectively constitute a batch of size 1. Hence, the numpy array is of shape `1x1x64x64`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size I0: (1, 1, 64, 64)\n",
      "Size I1: (1, 1, 64, 64)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Size I0:', I0_syn.shape)\n",
    "print('Size I1:', I1_syn.shape)\n",
    "plt.figure(figsize=(10,4))\n",
    "plt.subplot(121)\n",
    "plt.imshow(I0_syn[0,0,:,:],cmap='gray')\n",
    "plt.title('Source')\n",
    "plt.axis('off');\n",
    "plt.subplot(122)\n",
    "plt.imshow(I1_syn[0,0,:,:],cmap='gray')\n",
    "plt.title('Target')\n",
    "plt.axis('off');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, we can also create an example pair of realistic 2D MRI slices with spacing normalized to $[0,1]$. In that case, images are of size `1x1x128x128`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: Image WAS intensity normalized when loading: [0.0,1.367119]\n",
      "INFO: Normalizing the spacing to [0,1] in the largest dimension. (Turn normalize_spacing off if this is not desired.)\n",
      "Normalize spacing: [2. 2. 2.] -> [0.00787402 0.00787402 0.00787402]\n",
      "Normalize spacing, extent: [  0. 254. 254.] -> [0. 1. 1.]\n",
      "Normalize spacing: [2. 2.] -> [0.00787402 0.00787402]\n",
      "Normalize spacing, extent: [254. 254.] -> [1. 1.]\n",
      "INFO: Image WAS intensity normalized when loading: [0.0,1.4618253]\n",
      "INFO: Normalizing the spacing to [0,1] in the largest dimension. (Turn normalize_spacing off if this is not desired.)\n",
      "Normalize spacing: [2. 2. 2.] -> [0.00787402 0.00787402 0.00787402]\n",
      "Normalize spacing, extent: [  0. 254. 254.] -> [0. 1. 1.]\n",
      "Normalize spacing: [2. 2.] -> [0.00787402 0.00787402]\n",
      "Normalize spacing, extent: [254. 254.] -> [1. 1.]\n"
     ]
    }
   ],
   "source": [
    "I0_real, I1_real, spacing_real = EG.CreateRealExampleImages(dim).create_image_pair()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size I0: (1, 1, 128, 128)\n",
      "Size I1: (1, 1, 128, 128)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Size I0:', I0_real.shape)\n",
    "print('Size I1:', I1_real.shape)\n",
    "plt.figure(figsize=(10,4))\n",
    "plt.subplot(121)\n",
    "plt.imshow(I0_real[0,0,:,:],cmap='gray')\n",
    "plt.title('Source')\n",
    "plt.axis('off');\n",
    "plt.subplot(122)\n",
    "plt.imshow(I1_real[0,0,:,:],cmap='gray')\n",
    "plt.title('Target')\n",
    "plt.axis('off');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_image(I, title):\n",
    "    plt.imshow(I[0,0,:,:], cmap='gray')\n",
    "    plt.axis('off')\n",
    "    plt.title(title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we are ready to create an instance of `SI.RegisterImagePair` and show all available registration models to select from."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Known registration models are:\n",
      "------------------------------\n",
      "                              affine_map: map-based affine registration\n",
      "                           diffusion_map: displacement-based diffusion registration\n",
      "                           curvature_map: displacement-based curvature registration\n",
      "                     total_variation_map: displacement-based total variation registration\n",
      "                                 svf_map: map-based stationary velocity field\n",
      "                               svf_image: image-based stationary velocity field\n",
      "               svf_scalar_momentum_image: image-based stationary velocity field using the scalar momentum\n",
      "                 svf_scalar_momentum_map: map-based stationary velocity field using the scalar momentum\n",
      "               svf_vector_momentum_image: image-based stationary velocity field using the vector momentum\n",
      "                 svf_vector_momentum_map: map-based stationary velocity field using the vector momentum\n",
      "                      lddmm_shooting_map: map-based shooting-based LDDMM using the vector momentum\n",
      "                    lddmm_shooting_image: image-based shooting-based LDDMM using the vector momentum\n",
      "      lddmm_shooting_scalar_momentum_map: map-based shooting-based LDDMM using the scalar momentum\n",
      "    lddmm_shooting_scalar_momentum_image: image-based shooting-based LDDMM using the scalar momentum\n",
      "                svf_quasi_momentum_image: EXPERIMENTAL: Image-based SVF version which estimates velcocity based on a smoothed vetor field\n",
      "                lddmm_adapt_smoother_map: map-based shooting-based Region specific diffemorphic mapping, with a spatio-temporal regularizer\n",
      "                  svf_adapt_smoother_map: map-based shooting-based vSVF, with a spatio regularizer\n",
      "                            one_step_map: map based displacement field\n"
     ]
    }
   ],
   "source": [
    "si = SI.RegisterImagePair()\n",
    "si.print_available_models()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's basically it. We can now simply use the `register_images` method of `SI.RegisterImagePair` with appropriate parameters. \n",
    "\n",
    "In the following examples of different registration models, we will always keep track of all intermediate results by \n",
    "setting the `recording_step` to 1, i.e., we record the intermediate results in each optimization iteration and turn visualization off, as we will visualize the results later by hand. You can also set `recording_step` to `None` in which case nothing is recorded. \n",
    "\n",
    "Also, you can set `visualization_step` to a non-zero value which will produce an overview plot of the intermediate registration results at the given visualization steps.\n",
    "\n",
    "## Affine registration\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading parameter file = test2d_tst.json\n",
      "Overwriting key = use_map; category = root.model.deformation; value =  True -> True\n",
      "Overwriting key = type; category = root.model.registration_model; value =  svf_scalar_momentum_map -> affine_map\n",
      "Overwriting key = name; category = root.optimizer; value =  sgd -> sgd\n",
      "Overwriting key = nr_of_iterations; category = root.optimizer.single_scale; value =  50 -> 200\n",
      "Overwriting key = rel_ftol; category = root.optimizer.single_scale; value =  1e-07 -> 1e-07\n",
      "mapLowResFactor = 1: performing computations at original resolution.\n",
      "Overwriting key = use_map; category = root.model.deformation; value =  True -> True\n",
      "Overwriting key = map_low_res_factor; category = root.model.deformation; value =  1.0 -> 1.0\n",
      "Overwriting key = type; category = root.model.registration_model; value =  affine_map -> affine_map\n",
      "Overwriting key = type; category = root.model.registration_model; value =  affine_map -> affine_map\n",
      "Using map-based affine_map model\n",
      "works in mermaid iter mode\n",
      "AffineMapNet()\n",
      "Overwriting key = lr; category = root.optimizer.sgd.individual; value =  0.01 -> 0.001\n",
      "Overwriting key = lr; category = root.optimizer.sgd.shared; value =  0.01 -> 0.001\n",
      "Using ssd similarity measure\n",
      "\u001b[31m    0-Tot: E=013.2236 | simE=013.2236 | regE=000.0000 | optParE=000.0000 | relF=  n/a    | \u001b[0m\n",
      "\u001b[34m    0-Img: E=013.2236 | simE=013.2236 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m    1-Tot: E=020.9573 | simE=020.9573 | regE=000.0000 | optParE=000.0000 | relF=000.3522 | \u001b[0m\n",
      "\u001b[34m    1-Img: E=020.9573 | simE=020.9573 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m    2-Tot: E=021.1981 | simE=021.1981 | regE=000.0000 | optParE=000.0000 | relF=000.0108 | \u001b[0m\n",
      "\u001b[34m    2-Img: E=021.1981 | simE=021.1981 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m    3-Tot: E=019.1027 | simE=019.1027 | regE=000.0000 | optParE=000.0000 | relF=000.1042 | \u001b[0m\n",
      "\u001b[34m    3-Img: E=019.1027 | simE=019.1027 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m    4-Tot: E=013.9994 | simE=013.9994 | regE=000.0000 | optParE=000.0000 | relF=000.3402 | \u001b[0m\n",
      "\u001b[34m    4-Img: E=013.9994 | simE=013.9994 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m    5-Tot: E=013.7488 | simE=013.7488 | regE=000.0000 | optParE=000.0000 | relF=000.0170 | \u001b[0m\n",
      "\u001b[34m    5-Img: E=013.7488 | simE=013.7488 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m    6-Tot: E=013.4589 | simE=013.4589 | regE=000.0000 | optParE=000.0000 | relF=000.0201 | \u001b[0m\n",
      "\u001b[34m    6-Img: E=013.4589 | simE=013.4589 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m    7-Tot: E=012.9524 | simE=012.9524 | regE=000.0000 | optParE=000.0000 | relF=000.0363 | \u001b[0m\n",
      "\u001b[34m    7-Img: E=012.9524 | simE=012.9524 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m    8-Tot: E=012.3559 | simE=012.3559 | regE=000.0000 | optParE=000.0000 | relF=000.0447 | \u001b[0m\n",
      "\u001b[34m    8-Img: E=012.3559 | simE=012.3559 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m    9-Tot: E=012.5054 | simE=012.5054 | regE=000.0000 | optParE=000.0000 | relF=000.0111 | \u001b[0m\n",
      "\u001b[34m    9-Img: E=012.5054 | simE=012.5054 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   10-Tot: E=014.0539 | simE=014.0539 | regE=000.0000 | optParE=000.0000 | relF=000.1029 | \u001b[0m\n",
      "\u001b[34m   10-Img: E=014.0539 | simE=014.0539 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   11-Tot: E=012.1940 | simE=012.1940 | regE=000.0000 | optParE=000.0000 | relF=000.1410 | \u001b[0m\n",
      "\u001b[34m   11-Img: E=012.1940 | simE=012.1940 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   12-Tot: E=013.6777 | simE=013.6777 | regE=000.0000 | optParE=000.0000 | relF=000.1011 | \u001b[0m\n",
      "\u001b[34m   12-Img: E=013.6777 | simE=013.6777 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   13-Tot: E=010.3363 | simE=010.3363 | regE=000.0000 | optParE=000.0000 | relF=000.2947 | \u001b[0m\n",
      "\u001b[34m   13-Img: E=010.3363 | simE=010.3363 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   14-Tot: E=008.9969 | simE=008.9969 | regE=000.0000 | optParE=000.0000 | relF=000.1340 | \u001b[0m\n",
      "\u001b[34m   14-Img: E=008.9969 | simE=008.9969 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   15-Tot: E=007.8503 | simE=007.8503 | regE=000.0000 | optParE=000.0000 | relF=000.1296 | \u001b[0m\n",
      "\u001b[34m   15-Img: E=007.8503 | simE=007.8503 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   16-Tot: E=007.8507 | simE=007.8507 | regE=000.0000 | optParE=000.0000 | relF=000.0000 | \u001b[0m\n",
      "\u001b[34m   16-Img: E=007.8507 | simE=007.8507 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   17-Tot: E=022.9407 | simE=022.9407 | regE=000.0000 | optParE=000.0000 | relF=000.6303 | \u001b[0m\n",
      "\u001b[34m   17-Img: E=022.9407 | simE=022.9407 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   18-Tot: E=017.6973 | simE=017.6973 | regE=000.0000 | optParE=000.0000 | relF=000.2804 | \u001b[0m\n",
      "\u001b[34m   18-Img: E=017.6973 | simE=017.6973 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   19-Tot: E=019.8869 | simE=019.8869 | regE=000.0000 | optParE=000.0000 | relF=000.1048 | \u001b[0m\n",
      "\u001b[34m   19-Img: E=019.8869 | simE=019.8869 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   20-Tot: E=011.6989 | simE=011.6989 | regE=000.0000 | optParE=000.0000 | relF=000.6448 | \u001b[0m\n",
      "\u001b[34m   20-Img: E=011.6989 | simE=011.6989 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   21-Tot: E=014.3670 | simE=014.3670 | regE=000.0000 | optParE=000.0000 | relF=000.1736 | \u001b[0m\n",
      "\u001b[34m   21-Img: E=014.3670 | simE=014.3670 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   22-Tot: E=017.1848 | simE=017.1848 | regE=000.0000 | optParE=000.0000 | relF=000.1550 | \u001b[0m\n",
      "\u001b[34m   22-Img: E=017.1848 | simE=017.1848 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   23-Tot: E=012.2393 | simE=012.2393 | regE=000.0000 | optParE=000.0000 | relF=000.3735 | \u001b[0m\n",
      "\u001b[34m   23-Img: E=012.2393 | simE=012.2393 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   24-Tot: E=013.8287 | simE=013.8287 | regE=000.0000 | optParE=000.0000 | relF=000.1072 | \u001b[0m\n",
      "\u001b[34m   24-Img: E=013.8287 | simE=013.8287 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   25-Tot: E=014.4630 | simE=014.4630 | regE=000.0000 | optParE=000.0000 | relF=000.0410 | \u001b[0m\n",
      "\u001b[34m   25-Img: E=014.4630 | simE=014.4630 | regE=000.0000 |\u001b[0m\n",
      "Epoch    26: reducing learning rate of group 0 to 5.0000e-04.\n",
      "Epoch    26: reducing learning rate of group 1 to 5.0000e-04.\n",
      "\u001b[31m   26-Tot: E=011.0662 | simE=011.0662 | regE=000.0000 | optParE=000.0000 | relF=000.2815 | \u001b[0m\n",
      "\u001b[34m   26-Img: E=011.0662 | simE=011.0662 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   27-Tot: E=012.9243 | simE=012.9243 | regE=000.0000 | optParE=000.0000 | relF=000.1334 | \u001b[0m\n",
      "\u001b[34m   27-Img: E=012.9243 | simE=012.9243 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   28-Tot: E=011.4257 | simE=011.4257 | regE=000.0000 | optParE=000.0000 | relF=000.1206 | \u001b[0m\n",
      "\u001b[34m   28-Img: E=011.4257 | simE=011.4257 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   29-Tot: E=011.3729 | simE=011.3729 | regE=000.0000 | optParE=000.0000 | relF=000.0043 | \u001b[0m\n",
      "\u001b[34m   29-Img: E=011.3729 | simE=011.3729 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   30-Tot: E=011.2301 | simE=011.2301 | regE=000.0000 | optParE=000.0000 | relF=000.0117 | \u001b[0m\n",
      "\u001b[34m   30-Img: E=011.2301 | simE=011.2301 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   31-Tot: E=010.9019 | simE=010.9019 | regE=000.0000 | optParE=000.0000 | relF=000.0276 | \u001b[0m\n",
      "\u001b[34m   31-Img: E=010.9019 | simE=010.9019 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   32-Tot: E=010.4260 | simE=010.4260 | regE=000.0000 | optParE=000.0000 | relF=000.0417 | \u001b[0m\n",
      "\u001b[34m   32-Img: E=010.4260 | simE=010.4260 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   33-Tot: E=009.7560 | simE=009.7560 | regE=000.0000 | optParE=000.0000 | relF=000.0623 | \u001b[0m\n",
      "\u001b[34m   33-Img: E=009.7560 | simE=009.7560 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   34-Tot: E=009.0476 | simE=009.0476 | regE=000.0000 | optParE=000.0000 | relF=000.0705 | \u001b[0m\n",
      "\u001b[34m   34-Img: E=009.0476 | simE=009.0476 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   35-Tot: E=008.2595 | simE=008.2595 | regE=000.0000 | optParE=000.0000 | relF=000.0851 | \u001b[0m\n",
      "\u001b[34m   35-Img: E=008.2595 | simE=008.2595 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   36-Tot: E=007.1774 | simE=007.1774 | regE=000.0000 | optParE=000.0000 | relF=000.1323 | \u001b[0m\n",
      "\u001b[34m   36-Img: E=007.1774 | simE=007.1774 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   37-Tot: E=006.0097 | simE=006.0097 | regE=000.0000 | optParE=000.0000 | relF=000.1666 | \u001b[0m\n",
      "\u001b[34m   37-Img: E=006.0097 | simE=006.0097 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   38-Tot: E=007.3250 | simE=007.3250 | regE=000.0000 | optParE=000.0000 | relF=000.1580 | \u001b[0m\n",
      "\u001b[34m   38-Img: E=007.3250 | simE=007.3250 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   39-Tot: E=015.7316 | simE=015.7316 | regE=000.0000 | optParE=000.0000 | relF=000.5024 | \u001b[0m\n",
      "\u001b[34m   39-Img: E=015.7316 | simE=015.7316 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   40-Tot: E=013.5287 | simE=013.5287 | regE=000.0000 | optParE=000.0000 | relF=000.1516 | \u001b[0m\n",
      "\u001b[34m   40-Img: E=013.5287 | simE=013.5287 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   41-Tot: E=008.9899 | simE=008.9899 | regE=000.0000 | optParE=000.0000 | relF=000.4543 | \u001b[0m\n",
      "\u001b[34m   41-Img: E=008.9899 | simE=008.9899 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   42-Tot: E=019.7038 | simE=019.7038 | regE=000.0000 | optParE=000.0000 | relF=000.5175 | \u001b[0m\n",
      "\u001b[34m   42-Img: E=019.7038 | simE=019.7038 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   43-Tot: E=007.0539 | simE=007.0539 | regE=000.0000 | optParE=000.0000 | relF=001.5707 | \u001b[0m\n",
      "\u001b[34m   43-Img: E=007.0539 | simE=007.0539 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   44-Tot: E=025.5353 | simE=025.5353 | regE=000.0000 | optParE=000.0000 | relF=000.6965 | \u001b[0m\n",
      "\u001b[34m   44-Img: E=025.5353 | simE=025.5353 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   45-Tot: E=018.4514 | simE=018.4514 | regE=000.0000 | optParE=000.0000 | relF=000.3642 | \u001b[0m\n",
      "\u001b[34m   45-Img: E=018.4514 | simE=018.4514 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   46-Tot: E=014.4022 | simE=014.4022 | regE=000.0000 | optParE=000.0000 | relF=000.2629 | \u001b[0m\n",
      "\u001b[34m   46-Img: E=014.4022 | simE=014.4022 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   47-Tot: E=006.9669 | simE=006.9669 | regE=000.0000 | optParE=000.0000 | relF=000.9333 | \u001b[0m\n",
      "\u001b[34m   47-Img: E=006.9669 | simE=006.9669 | regE=000.0000 |\u001b[0m\n",
      "Epoch    48: reducing learning rate of group 0 to 2.5000e-04.\n",
      "Epoch    48: reducing learning rate of group 1 to 2.5000e-04.\n",
      "\u001b[31m   48-Tot: E=024.7832 | simE=024.7832 | regE=000.0000 | optParE=000.0000 | relF=000.6910 | \u001b[0m\n",
      "\u001b[34m   48-Img: E=024.7832 | simE=024.7832 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   49-Tot: E=019.5329 | simE=019.5329 | regE=000.0000 | optParE=000.0000 | relF=000.2557 | \u001b[0m\n",
      "\u001b[34m   49-Img: E=019.5329 | simE=019.5329 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   50-Tot: E=011.5676 | simE=011.5676 | regE=000.0000 | optParE=000.0000 | relF=000.6338 | \u001b[0m\n",
      "\u001b[34m   50-Img: E=011.5676 | simE=011.5676 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   51-Tot: E=005.9451 | simE=005.9451 | regE=000.0000 | optParE=000.0000 | relF=000.8096 | \u001b[0m\n",
      "\u001b[34m   51-Img: E=005.9451 | simE=005.9451 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   52-Tot: E=007.5563 | simE=007.5563 | regE=000.0000 | optParE=000.0000 | relF=000.1883 | \u001b[0m\n",
      "\u001b[34m   52-Img: E=007.5563 | simE=007.5563 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   53-Tot: E=006.3398 | simE=006.3398 | regE=000.0000 | optParE=000.0000 | relF=000.1657 | \u001b[0m\n",
      "\u001b[34m   53-Img: E=006.3398 | simE=006.3398 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   54-Tot: E=006.4094 | simE=006.4094 | regE=000.0000 | optParE=000.0000 | relF=000.0094 | \u001b[0m\n",
      "\u001b[34m   54-Img: E=006.4094 | simE=006.4094 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   55-Tot: E=006.1746 | simE=006.1746 | regE=000.0000 | optParE=000.0000 | relF=000.0327 | \u001b[0m\n",
      "\u001b[34m   55-Img: E=006.1746 | simE=006.1746 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   56-Tot: E=005.8178 | simE=005.8178 | regE=000.0000 | optParE=000.0000 | relF=000.0523 | \u001b[0m\n",
      "\u001b[34m   56-Img: E=005.8178 | simE=005.8178 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   57-Tot: E=005.2904 | simE=005.2904 | regE=000.0000 | optParE=000.0000 | relF=000.0839 | \u001b[0m\n",
      "\u001b[34m   57-Img: E=005.2904 | simE=005.2904 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   58-Tot: E=006.2699 | simE=006.2699 | regE=000.0000 | optParE=000.0000 | relF=000.1347 | \u001b[0m\n",
      "\u001b[34m   58-Img: E=006.2699 | simE=006.2699 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   59-Tot: E=004.5827 | simE=004.5827 | regE=000.0000 | optParE=000.0000 | relF=000.3022 | \u001b[0m\n",
      "\u001b[34m   59-Img: E=004.5827 | simE=004.5827 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   60-Tot: E=006.7536 | simE=006.7536 | regE=000.0000 | optParE=000.0000 | relF=000.2800 | \u001b[0m\n",
      "\u001b[34m   60-Img: E=006.7536 | simE=006.7536 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   61-Tot: E=004.3073 | simE=004.3073 | regE=000.0000 | optParE=000.0000 | relF=000.4609 | \u001b[0m\n",
      "\u001b[34m   61-Img: E=004.3073 | simE=004.3073 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   62-Tot: E=009.1613 | simE=009.1613 | regE=000.0000 | optParE=000.0000 | relF=000.4777 | \u001b[0m\n",
      "\u001b[34m   62-Img: E=009.1613 | simE=009.1613 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   63-Tot: E=007.1950 | simE=007.1950 | regE=000.0000 | optParE=000.0000 | relF=000.2399 | \u001b[0m\n",
      "\u001b[34m   63-Img: E=007.1950 | simE=007.1950 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   64-Tot: E=006.4788 | simE=006.4788 | regE=000.0000 | optParE=000.0000 | relF=000.0958 | \u001b[0m\n",
      "\u001b[34m   64-Img: E=006.4788 | simE=006.4788 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   65-Tot: E=007.3137 | simE=007.3137 | regE=000.0000 | optParE=000.0000 | relF=000.1004 | \u001b[0m\n",
      "\u001b[34m   65-Img: E=007.3137 | simE=007.3137 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   66-Tot: E=007.6215 | simE=007.6215 | regE=000.0000 | optParE=000.0000 | relF=000.0357 | \u001b[0m\n",
      "\u001b[34m   66-Img: E=007.6215 | simE=007.6215 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   67-Tot: E=008.2021 | simE=008.2021 | regE=000.0000 | optParE=000.0000 | relF=000.0631 | \u001b[0m\n",
      "\u001b[34m   67-Img: E=008.2021 | simE=008.2021 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   68-Tot: E=007.0299 | simE=007.0299 | regE=000.0000 | optParE=000.0000 | relF=000.1460 | \u001b[0m\n",
      "\u001b[34m   68-Img: E=007.0299 | simE=007.0299 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   69-Tot: E=007.6136 | simE=007.6136 | regE=000.0000 | optParE=000.0000 | relF=000.0678 | \u001b[0m\n",
      "\u001b[34m   69-Img: E=007.6136 | simE=007.6136 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   70-Tot: E=006.5765 | simE=006.5765 | regE=000.0000 | optParE=000.0000 | relF=000.1369 | \u001b[0m\n",
      "\u001b[34m   70-Img: E=006.5765 | simE=006.5765 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   71-Tot: E=009.2278 | simE=009.2278 | regE=000.0000 | optParE=000.0000 | relF=000.2592 | \u001b[0m\n",
      "\u001b[34m   71-Img: E=009.2278 | simE=009.2278 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   72-Tot: E=003.1484 | simE=003.1484 | regE=000.0000 | optParE=000.0000 | relF=001.4654 | \u001b[0m\n",
      "\u001b[34m   72-Img: E=003.1484 | simE=003.1484 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   73-Tot: E=014.2395 | simE=014.2395 | regE=000.0000 | optParE=000.0000 | relF=000.7278 | \u001b[0m\n",
      "\u001b[34m   73-Img: E=014.2395 | simE=014.2395 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   74-Tot: E=005.3620 | simE=005.3620 | regE=000.0000 | optParE=000.0000 | relF=001.3954 | \u001b[0m\n",
      "\u001b[34m   74-Img: E=005.3620 | simE=005.3620 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   75-Tot: E=011.2567 | simE=011.2567 | regE=000.0000 | optParE=000.0000 | relF=000.4809 | \u001b[0m\n",
      "\u001b[34m   75-Img: E=011.2567 | simE=011.2567 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   76-Tot: E=003.9892 | simE=003.9892 | regE=000.0000 | optParE=000.0000 | relF=001.4566 | \u001b[0m\n",
      "\u001b[34m   76-Img: E=003.9892 | simE=003.9892 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   77-Tot: E=010.5021 | simE=010.5021 | regE=000.0000 | optParE=000.0000 | relF=000.5662 | \u001b[0m\n",
      "\u001b[34m   77-Img: E=010.5021 | simE=010.5021 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   78-Tot: E=005.7813 | simE=005.7813 | regE=000.0000 | optParE=000.0000 | relF=000.6961 | \u001b[0m\n",
      "\u001b[34m   78-Img: E=005.7813 | simE=005.7813 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   79-Tot: E=004.6732 | simE=004.6732 | regE=000.0000 | optParE=000.0000 | relF=000.1953 | \u001b[0m\n",
      "\u001b[34m   79-Img: E=004.6732 | simE=004.6732 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   80-Tot: E=005.9160 | simE=005.9160 | regE=000.0000 | optParE=000.0000 | relF=000.1797 | \u001b[0m\n",
      "\u001b[34m   80-Img: E=005.9160 | simE=005.9160 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   81-Tot: E=004.5572 | simE=004.5572 | regE=000.0000 | optParE=000.0000 | relF=000.2445 | \u001b[0m\n",
      "\u001b[34m   81-Img: E=004.5572 | simE=004.5572 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   82-Tot: E=008.2272 | simE=008.2272 | regE=000.0000 | optParE=000.0000 | relF=000.3977 | \u001b[0m\n",
      "\u001b[34m   82-Img: E=008.2272 | simE=008.2272 | regE=000.0000 |\u001b[0m\n",
      "Epoch    83: reducing learning rate of group 0 to 1.2500e-04.\n",
      "Epoch    83: reducing learning rate of group 1 to 1.2500e-04.\n",
      "\u001b[31m   83-Tot: E=005.4178 | simE=005.4178 | regE=000.0000 | optParE=000.0000 | relF=000.4378 | \u001b[0m\n",
      "\u001b[34m   83-Img: E=005.4178 | simE=005.4178 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   84-Tot: E=005.8283 | simE=005.8283 | regE=000.0000 | optParE=000.0000 | relF=000.0601 | \u001b[0m\n",
      "\u001b[34m   84-Img: E=005.8283 | simE=005.8283 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   85-Tot: E=003.9225 | simE=003.9225 | regE=000.0000 | optParE=000.0000 | relF=000.3872 | \u001b[0m\n",
      "\u001b[34m   85-Img: E=003.9225 | simE=003.9225 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   86-Tot: E=005.1337 | simE=005.1337 | regE=000.0000 | optParE=000.0000 | relF=000.1975 | \u001b[0m\n",
      "\u001b[34m   86-Img: E=005.1337 | simE=005.1337 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   87-Tot: E=004.1200 | simE=004.1200 | regE=000.0000 | optParE=000.0000 | relF=000.1980 | \u001b[0m\n",
      "\u001b[34m   87-Img: E=004.1200 | simE=004.1200 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   88-Tot: E=004.1852 | simE=004.1852 | regE=000.0000 | optParE=000.0000 | relF=000.0126 | \u001b[0m\n",
      "\u001b[34m   88-Img: E=004.1852 | simE=004.1852 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   89-Tot: E=003.5298 | simE=003.5298 | regE=000.0000 | optParE=000.0000 | relF=000.1447 | \u001b[0m\n",
      "\u001b[34m   89-Img: E=003.5298 | simE=003.5298 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   90-Tot: E=003.5741 | simE=003.5741 | regE=000.0000 | optParE=000.0000 | relF=000.0097 | \u001b[0m\n",
      "\u001b[34m   90-Img: E=003.5741 | simE=003.5741 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   91-Tot: E=004.6154 | simE=004.6154 | regE=000.0000 | optParE=000.0000 | relF=000.1854 | \u001b[0m\n",
      "\u001b[34m   91-Img: E=004.6154 | simE=004.6154 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   92-Tot: E=002.2712 | simE=002.2712 | regE=000.0000 | optParE=000.0000 | relF=000.7166 | \u001b[0m\n",
      "\u001b[34m   92-Img: E=002.2712 | simE=002.2712 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   93-Tot: E=001.9037 | simE=001.9037 | regE=000.0000 | optParE=000.0000 | relF=000.1266 | \u001b[0m\n",
      "\u001b[34m   93-Img: E=001.9037 | simE=001.9037 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   94-Tot: E=002.2968 | simE=002.2968 | regE=000.0000 | optParE=000.0000 | relF=000.1192 | \u001b[0m\n",
      "\u001b[34m   94-Img: E=002.2968 | simE=002.2968 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   95-Tot: E=006.1827 | simE=006.1827 | regE=000.0000 | optParE=000.0000 | relF=000.5410 | \u001b[0m\n",
      "\u001b[34m   95-Img: E=006.1827 | simE=006.1827 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   96-Tot: E=004.3438 | simE=004.3438 | regE=000.0000 | optParE=000.0000 | relF=000.3441 | \u001b[0m\n",
      "\u001b[34m   96-Img: E=004.3438 | simE=004.3438 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   97-Tot: E=005.0088 | simE=005.0088 | regE=000.0000 | optParE=000.0000 | relF=000.1107 | \u001b[0m\n",
      "\u001b[34m   97-Img: E=005.0088 | simE=005.0088 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   98-Tot: E=005.5938 | simE=005.5938 | regE=000.0000 | optParE=000.0000 | relF=000.0887 | \u001b[0m\n",
      "\u001b[34m   98-Img: E=005.5938 | simE=005.5938 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m   99-Tot: E=002.5876 | simE=002.5876 | regE=000.0000 | optParE=000.0000 | relF=000.8380 | \u001b[0m\n",
      "\u001b[34m   99-Img: E=002.5876 | simE=002.5876 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  100-Tot: E=003.6892 | simE=003.6892 | regE=000.0000 | optParE=000.0000 | relF=000.2349 | \u001b[0m\n",
      "\u001b[34m  100-Img: E=003.6892 | simE=003.6892 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  101-Tot: E=003.7319 | simE=003.7319 | regE=000.0000 | optParE=000.0000 | relF=000.0090 | \u001b[0m\n",
      "\u001b[34m  101-Img: E=003.7319 | simE=003.7319 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  102-Tot: E=003.5575 | simE=003.5575 | regE=000.0000 | optParE=000.0000 | relF=000.0383 | \u001b[0m\n",
      "\u001b[34m  102-Img: E=003.5575 | simE=003.5575 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  103-Tot: E=003.6540 | simE=003.6540 | regE=000.0000 | optParE=000.0000 | relF=000.0207 | \u001b[0m\n",
      "\u001b[34m  103-Img: E=003.6540 | simE=003.6540 | regE=000.0000 |\u001b[0m\n",
      "Epoch   104: reducing learning rate of group 0 to 6.2500e-05.\n",
      "Epoch   104: reducing learning rate of group 1 to 6.2500e-05.\n",
      "\u001b[31m  104-Tot: E=003.2122 | simE=003.2122 | regE=000.0000 | optParE=000.0000 | relF=000.1049 | \u001b[0m\n",
      "\u001b[34m  104-Img: E=003.2122 | simE=003.2122 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  105-Tot: E=004.2998 | simE=004.2998 | regE=000.0000 | optParE=000.0000 | relF=000.2052 | \u001b[0m\n",
      "\u001b[34m  105-Img: E=004.2998 | simE=004.2998 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  106-Tot: E=001.2616 | simE=001.2616 | regE=000.0000 | optParE=000.0000 | relF=001.3434 | \u001b[0m\n",
      "\u001b[34m  106-Img: E=001.2616 | simE=001.2616 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  107-Tot: E=001.6095 | simE=001.6095 | regE=000.0000 | optParE=000.0000 | relF=000.1333 | \u001b[0m\n",
      "\u001b[34m  107-Img: E=001.6095 | simE=001.6095 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  108-Tot: E=001.1994 | simE=001.1994 | regE=000.0000 | optParE=000.0000 | relF=000.1864 | \u001b[0m\n",
      "\u001b[34m  108-Img: E=001.1994 | simE=001.1994 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  109-Tot: E=001.2216 | simE=001.2216 | regE=000.0000 | optParE=000.0000 | relF=000.0100 | \u001b[0m\n",
      "\u001b[34m  109-Img: E=001.2216 | simE=001.2216 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  110-Tot: E=001.3448 | simE=001.3448 | regE=000.0000 | optParE=000.0000 | relF=000.0525 | \u001b[0m\n",
      "\u001b[34m  110-Img: E=001.3448 | simE=001.3448 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  111-Tot: E=001.4939 | simE=001.4939 | regE=000.0000 | optParE=000.0000 | relF=000.0598 | \u001b[0m\n",
      "\u001b[34m  111-Img: E=001.4939 | simE=001.4939 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  112-Tot: E=000.8310 | simE=000.8310 | regE=000.0000 | optParE=000.0000 | relF=000.3621 | \u001b[0m\n",
      "\u001b[34m  112-Img: E=000.8310 | simE=000.8310 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  113-Tot: E=000.8712 | simE=000.8712 | regE=000.0000 | optParE=000.0000 | relF=000.0215 | \u001b[0m\n",
      "\u001b[34m  113-Img: E=000.8712 | simE=000.8712 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  114-Tot: E=001.0961 | simE=001.0961 | regE=000.0000 | optParE=000.0000 | relF=000.1073 | \u001b[0m\n",
      "\u001b[34m  114-Img: E=001.0961 | simE=001.0961 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  115-Tot: E=001.2494 | simE=001.2494 | regE=000.0000 | optParE=000.0000 | relF=000.0682 | \u001b[0m\n",
      "\u001b[34m  115-Img: E=001.2494 | simE=001.2494 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  116-Tot: E=001.5184 | simE=001.5184 | regE=000.0000 | optParE=000.0000 | relF=000.1068 | \u001b[0m\n",
      "\u001b[34m  116-Img: E=001.5184 | simE=001.5184 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  117-Tot: E=002.0468 | simE=002.0468 | regE=000.0000 | optParE=000.0000 | relF=000.1734 | \u001b[0m\n",
      "\u001b[34m  117-Img: E=002.0468 | simE=002.0468 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  118-Tot: E=002.8258 | simE=002.8258 | regE=000.0000 | optParE=000.0000 | relF=000.2036 | \u001b[0m\n",
      "\u001b[34m  118-Img: E=002.8258 | simE=002.8258 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  119-Tot: E=000.9048 | simE=000.9048 | regE=000.0000 | optParE=000.0000 | relF=001.0085 | \u001b[0m\n",
      "\u001b[34m  119-Img: E=000.9048 | simE=000.9048 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  120-Tot: E=000.8803 | simE=000.8803 | regE=000.0000 | optParE=000.0000 | relF=000.0130 | \u001b[0m\n",
      "\u001b[34m  120-Img: E=000.8803 | simE=000.8803 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  121-Tot: E=001.1421 | simE=001.1421 | regE=000.0000 | optParE=000.0000 | relF=000.1222 | \u001b[0m\n",
      "\u001b[34m  121-Img: E=001.1421 | simE=001.1421 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  122-Tot: E=001.2982 | simE=001.2982 | regE=000.0000 | optParE=000.0000 | relF=000.0679 | \u001b[0m\n",
      "\u001b[34m  122-Img: E=001.2982 | simE=001.2982 | regE=000.0000 |\u001b[0m\n",
      "Epoch   123: reducing learning rate of group 0 to 3.1250e-05.\n",
      "Epoch   123: reducing learning rate of group 1 to 3.1250e-05.\n",
      "\u001b[31m  123-Tot: E=001.7958 | simE=001.7958 | regE=000.0000 | optParE=000.0000 | relF=000.1780 | \u001b[0m\n",
      "\u001b[34m  123-Img: E=001.7958 | simE=001.7958 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  124-Tot: E=002.4916 | simE=002.4916 | regE=000.0000 | optParE=000.0000 | relF=000.1993 | \u001b[0m\n",
      "\u001b[34m  124-Img: E=002.4916 | simE=002.4916 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  125-Tot: E=000.7670 | simE=000.7670 | regE=000.0000 | optParE=000.0000 | relF=000.9760 | \u001b[0m\n",
      "\u001b[34m  125-Img: E=000.7670 | simE=000.7670 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  126-Tot: E=000.8301 | simE=000.8301 | regE=000.0000 | optParE=000.0000 | relF=000.0345 | \u001b[0m\n",
      "\u001b[34m  126-Img: E=000.8301 | simE=000.8301 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  127-Tot: E=000.6840 | simE=000.6840 | regE=000.0000 | optParE=000.0000 | relF=000.0868 | \u001b[0m\n",
      "\u001b[34m  127-Img: E=000.6840 | simE=000.6840 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  128-Tot: E=000.6126 | simE=000.6126 | regE=000.0000 | optParE=000.0000 | relF=000.0443 | \u001b[0m\n",
      "\u001b[34m  128-Img: E=000.6126 | simE=000.6126 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  129-Tot: E=000.6030 | simE=000.6030 | regE=000.0000 | optParE=000.0000 | relF=000.0060 | \u001b[0m\n",
      "\u001b[34m  129-Img: E=000.6030 | simE=000.6030 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  130-Tot: E=000.5925 | simE=000.5925 | regE=000.0000 | optParE=000.0000 | relF=000.0066 | \u001b[0m\n",
      "\u001b[34m  130-Img: E=000.5925 | simE=000.5925 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  131-Tot: E=000.5859 | simE=000.5859 | regE=000.0000 | optParE=000.0000 | relF=000.0042 | \u001b[0m\n",
      "\u001b[34m  131-Img: E=000.5859 | simE=000.5859 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  132-Tot: E=000.5814 | simE=000.5814 | regE=000.0000 | optParE=000.0000 | relF=000.0028 | \u001b[0m\n",
      "\u001b[34m  132-Img: E=000.5814 | simE=000.5814 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  133-Tot: E=000.5789 | simE=000.5789 | regE=000.0000 | optParE=000.0000 | relF=000.0016 | \u001b[0m\n",
      "\u001b[34m  133-Img: E=000.5789 | simE=000.5789 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  134-Tot: E=000.5780 | simE=000.5780 | regE=000.0000 | optParE=000.0000 | relF=000.0006 | \u001b[0m\n",
      "\u001b[34m  134-Img: E=000.5780 | simE=000.5780 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  135-Tot: E=000.5785 | simE=000.5785 | regE=000.0000 | optParE=000.0000 | relF=000.0003 | \u001b[0m\n",
      "\u001b[34m  135-Img: E=000.5785 | simE=000.5785 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  136-Tot: E=000.5797 | simE=000.5797 | regE=000.0000 | optParE=000.0000 | relF=000.0008 | \u001b[0m\n",
      "\u001b[34m  136-Img: E=000.5797 | simE=000.5797 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  137-Tot: E=000.5813 | simE=000.5813 | regE=000.0000 | optParE=000.0000 | relF=000.0010 | \u001b[0m\n",
      "\u001b[34m  137-Img: E=000.5813 | simE=000.5813 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  138-Tot: E=000.5826 | simE=000.5826 | regE=000.0000 | optParE=000.0000 | relF=000.0009 | \u001b[0m\n",
      "\u001b[34m  138-Img: E=000.5826 | simE=000.5826 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  139-Tot: E=000.5836 | simE=000.5836 | regE=000.0000 | optParE=000.0000 | relF=000.0006 | \u001b[0m\n",
      "\u001b[34m  139-Img: E=000.5836 | simE=000.5836 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  140-Tot: E=000.5840 | simE=000.5840 | regE=000.0000 | optParE=000.0000 | relF=000.0003 | \u001b[0m\n",
      "\u001b[34m  140-Img: E=000.5840 | simE=000.5840 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  141-Tot: E=000.5839 | simE=000.5839 | regE=000.0000 | optParE=000.0000 | relF=000.0000 | \u001b[0m\n",
      "\u001b[34m  141-Img: E=000.5839 | simE=000.5839 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  142-Tot: E=000.5835 | simE=000.5835 | regE=000.0000 | optParE=000.0000 | relF=000.0003 | \u001b[0m\n",
      "\u001b[34m  142-Img: E=000.5835 | simE=000.5835 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  143-Tot: E=000.5826 | simE=000.5826 | regE=000.0000 | optParE=000.0000 | relF=000.0005 | \u001b[0m\n",
      "\u001b[34m  143-Img: E=000.5826 | simE=000.5826 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  144-Tot: E=000.5816 | simE=000.5816 | regE=000.0000 | optParE=000.0000 | relF=000.0007 | \u001b[0m\n",
      "\u001b[34m  144-Img: E=000.5816 | simE=000.5816 | regE=000.0000 |\u001b[0m\n",
      "Epoch   145: reducing learning rate of group 0 to 1.5625e-05.\n",
      "Epoch   145: reducing learning rate of group 1 to 1.5625e-05.\n",
      "\u001b[31m  145-Tot: E=000.5803 | simE=000.5803 | regE=000.0000 | optParE=000.0000 | relF=000.0008 | \u001b[0m\n",
      "\u001b[34m  145-Img: E=000.5803 | simE=000.5803 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  146-Tot: E=000.5791 | simE=000.5791 | regE=000.0000 | optParE=000.0000 | relF=000.0007 | \u001b[0m\n",
      "\u001b[34m  146-Img: E=000.5791 | simE=000.5791 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  147-Tot: E=000.5786 | simE=000.5786 | regE=000.0000 | optParE=000.0000 | relF=000.0003 | \u001b[0m\n",
      "\u001b[34m  147-Img: E=000.5786 | simE=000.5786 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  148-Tot: E=000.5781 | simE=000.5781 | regE=000.0000 | optParE=000.0000 | relF=000.0003 | \u001b[0m\n",
      "\u001b[34m  148-Img: E=000.5781 | simE=000.5781 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  149-Tot: E=000.5777 | simE=000.5777 | regE=000.0000 | optParE=000.0000 | relF=000.0003 | \u001b[0m\n",
      "\u001b[34m  149-Img: E=000.5777 | simE=000.5777 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  150-Tot: E=000.5773 | simE=000.5773 | regE=000.0000 | optParE=000.0000 | relF=000.0002 | \u001b[0m\n",
      "\u001b[34m  150-Img: E=000.5773 | simE=000.5773 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  151-Tot: E=000.5770 | simE=000.5770 | regE=000.0000 | optParE=000.0000 | relF=000.0002 | \u001b[0m\n",
      "\u001b[34m  151-Img: E=000.5770 | simE=000.5770 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  152-Tot: E=000.5768 | simE=000.5768 | regE=000.0000 | optParE=000.0000 | relF=000.0001 | \u001b[0m\n",
      "\u001b[34m  152-Img: E=000.5768 | simE=000.5768 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  153-Tot: E=000.5766 | simE=000.5766 | regE=000.0000 | optParE=000.0000 | relF=000.0001 | \u001b[0m\n",
      "\u001b[34m  153-Img: E=000.5766 | simE=000.5766 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  154-Tot: E=000.5765 | simE=000.5765 | regE=000.0000 | optParE=000.0000 | relF=000.0001 | \u001b[0m\n",
      "\u001b[34m  154-Img: E=000.5765 | simE=000.5765 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  155-Tot: E=000.5765 | simE=000.5765 | regE=000.0000 | optParE=000.0000 | relF=000.0000 | \u001b[0m\n",
      "\u001b[34m  155-Img: E=000.5765 | simE=000.5765 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  156-Tot: E=000.5765 | simE=000.5765 | regE=000.0000 | optParE=000.0000 | relF=000.0000 | \u001b[0m\n",
      "\u001b[34m  156-Img: E=000.5765 | simE=000.5765 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  157-Tot: E=000.5765 | simE=000.5765 | regE=000.0000 | optParE=000.0000 | relF=000.0000 | \u001b[0m\n",
      "\u001b[34m  157-Img: E=000.5765 | simE=000.5765 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  158-Tot: E=000.5766 | simE=000.5766 | regE=000.0000 | optParE=000.0000 | relF=000.0000 | \u001b[0m\n",
      "\u001b[34m  158-Img: E=000.5766 | simE=000.5766 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  159-Tot: E=000.5766 | simE=000.5766 | regE=000.0000 | optParE=000.0000 | relF=000.0000 | \u001b[0m\n",
      "\u001b[34m  159-Img: E=000.5766 | simE=000.5766 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  160-Tot: E=000.5766 | simE=000.5766 | regE=000.0000 | optParE=000.0000 | relF=000.0000 | \u001b[0m\n",
      "\u001b[34m  160-Img: E=000.5766 | simE=000.5766 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  161-Tot: E=000.5767 | simE=000.5767 | regE=000.0000 | optParE=000.0000 | relF=000.0000 | \u001b[0m\n",
      "\u001b[34m  161-Img: E=000.5767 | simE=000.5767 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  162-Tot: E=000.5767 | simE=000.5767 | regE=000.0000 | optParE=000.0000 | relF=000.0000 | \u001b[0m\n",
      "\u001b[34m  162-Img: E=000.5767 | simE=000.5767 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  163-Tot: E=000.5767 | simE=000.5767 | regE=000.0000 | optParE=000.0000 | relF=000.0000 | \u001b[0m\n",
      "\u001b[34m  163-Img: E=000.5767 | simE=000.5767 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  164-Tot: E=000.5767 | simE=000.5767 | regE=000.0000 | optParE=000.0000 | relF=000.0000 | \u001b[0m\n",
      "\u001b[34m  164-Img: E=000.5767 | simE=000.5767 | regE=000.0000 |\u001b[0m\n",
      "Epoch   165: reducing learning rate of group 0 to 7.8125e-06.\n",
      "Epoch   165: reducing learning rate of group 1 to 7.8125e-06.\n",
      "\u001b[31m  165-Tot: E=000.5767 | simE=000.5767 | regE=000.0000 | optParE=000.0000 | relF=000.0000 | \u001b[0m\n",
      "\u001b[34m  165-Img: E=000.5767 | simE=000.5767 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  166-Tot: E=000.5767 | simE=000.5767 | regE=000.0000 | optParE=000.0000 | relF=000.0000 | \u001b[0m\n",
      "\u001b[34m  166-Img: E=000.5767 | simE=000.5767 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  167-Tot: E=000.5767 | simE=000.5767 | regE=000.0000 | optParE=000.0000 | relF=000.0000 | \u001b[0m\n",
      "\u001b[34m  167-Img: E=000.5767 | simE=000.5767 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  168-Tot: E=000.5766 | simE=000.5766 | regE=000.0000 | optParE=000.0000 | relF=000.0000 | \u001b[0m\n",
      "\u001b[34m  168-Img: E=000.5766 | simE=000.5766 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  169-Tot: E=000.5766 | simE=000.5766 | regE=000.0000 | optParE=000.0000 | relF=000.0000 | \u001b[0m\n",
      "\u001b[34m  169-Img: E=000.5766 | simE=000.5766 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  170-Tot: E=000.5766 | simE=000.5766 | regE=000.0000 | optParE=000.0000 | relF=000.0000 | \u001b[0m\n",
      "\u001b[34m  170-Img: E=000.5766 | simE=000.5766 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  171-Tot: E=000.5766 | simE=000.5766 | regE=000.0000 | optParE=000.0000 | relF=000.0000 | \u001b[0m\n",
      "\u001b[34m  171-Img: E=000.5766 | simE=000.5766 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  172-Tot: E=000.5766 | simE=000.5766 | regE=000.0000 | optParE=000.0000 | relF=000.0000 | \u001b[0m\n",
      "\u001b[34m  172-Img: E=000.5766 | simE=000.5766 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  173-Tot: E=000.5766 | simE=000.5766 | regE=000.0000 | optParE=000.0000 | relF=000.0000 | \u001b[0m\n",
      "\u001b[34m  173-Img: E=000.5766 | simE=000.5766 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  174-Tot: E=000.5765 | simE=000.5765 | regE=000.0000 | optParE=000.0000 | relF=000.0000 | \u001b[0m\n",
      "\u001b[34m  174-Img: E=000.5765 | simE=000.5765 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  175-Tot: E=000.5765 | simE=000.5765 | regE=000.0000 | optParE=000.0000 | relF=000.0000 | \u001b[0m\n",
      "\u001b[34m  175-Img: E=000.5765 | simE=000.5765 | regE=000.0000 |\u001b[0m\n",
      "Epoch   176: reducing learning rate of group 0 to 3.9063e-06.\n",
      "Epoch   176: reducing learning rate of group 1 to 3.9063e-06.\n",
      "\u001b[31m  176-Tot: E=000.5765 | simE=000.5765 | regE=000.0000 | optParE=000.0000 | relF=000.0000 | \u001b[0m\n",
      "\u001b[34m  176-Img: E=000.5765 | simE=000.5765 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  177-Tot: E=000.5765 | simE=000.5765 | regE=000.0000 | optParE=000.0000 | relF=000.0000 | \u001b[0m\n",
      "\u001b[34m  177-Img: E=000.5765 | simE=000.5765 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  178-Tot: E=000.5765 | simE=000.5765 | regE=000.0000 | optParE=000.0000 | relF=000.0000 | \u001b[0m\n",
      "\u001b[34m  178-Img: E=000.5765 | simE=000.5765 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  179-Tot: E=000.5765 | simE=000.5765 | regE=000.0000 | optParE=000.0000 | relF=000.0000 | \u001b[0m\n",
      "\u001b[34m  179-Img: E=000.5765 | simE=000.5765 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  180-Tot: E=000.5765 | simE=000.5765 | regE=000.0000 | optParE=000.0000 | relF=000.0000 | \u001b[0m\n",
      "\u001b[34m  180-Img: E=000.5765 | simE=000.5765 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  181-Tot: E=000.5765 | simE=000.5765 | regE=000.0000 | optParE=000.0000 | relF=000.0000 | \u001b[0m\n",
      "\u001b[34m  181-Img: E=000.5765 | simE=000.5765 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  182-Tot: E=000.5765 | simE=000.5765 | regE=000.0000 | optParE=000.0000 | relF=000.0000 | \u001b[0m\n",
      "\u001b[34m  182-Img: E=000.5765 | simE=000.5765 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  183-Tot: E=000.5765 | simE=000.5765 | regE=000.0000 | optParE=000.0000 | relF=000.0000 | \u001b[0m\n",
      "\u001b[34m  183-Img: E=000.5765 | simE=000.5765 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  184-Tot: E=000.5765 | simE=000.5765 | regE=000.0000 | optParE=000.0000 | relF=000.0000 | \u001b[0m\n",
      "\u001b[34m  184-Img: E=000.5765 | simE=000.5765 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  185-Tot: E=000.5765 | simE=000.5765 | regE=000.0000 | optParE=000.0000 | relF=000.0000 | \u001b[0m\n",
      "\u001b[34m  185-Img: E=000.5765 | simE=000.5765 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  186-Tot: E=000.5765 | simE=000.5765 | regE=000.0000 | optParE=000.0000 | relF=000.0000 | \u001b[0m\n",
      "\u001b[34m  186-Img: E=000.5765 | simE=000.5765 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  187-Tot: E=000.5765 | simE=000.5765 | regE=000.0000 | optParE=000.0000 | relF=000.0000 | \u001b[0m\n",
      "\u001b[34m  187-Img: E=000.5765 | simE=000.5765 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  188-Tot: E=000.5765 | simE=000.5765 | regE=000.0000 | optParE=000.0000 | relF=000.0000 | \u001b[0m\n",
      "\u001b[34m  188-Img: E=000.5765 | simE=000.5765 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  189-Tot: E=000.5765 | simE=000.5765 | regE=000.0000 | optParE=000.0000 | relF=000.0000 | \u001b[0m\n",
      "\u001b[34m  189-Img: E=000.5765 | simE=000.5765 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  190-Tot: E=000.5765 | simE=000.5765 | regE=000.0000 | optParE=000.0000 | relF=000.0000 | \u001b[0m\n",
      "\u001b[34m  190-Img: E=000.5765 | simE=000.5765 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  191-Tot: E=000.5765 | simE=000.5765 | regE=000.0000 | optParE=000.0000 | relF=000.0000 | \u001b[0m\n",
      "\u001b[34m  191-Img: E=000.5765 | simE=000.5765 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  192-Tot: E=000.5765 | simE=000.5765 | regE=000.0000 | optParE=000.0000 | relF=000.0000 | \u001b[0m\n",
      "\u001b[34m  192-Img: E=000.5765 | simE=000.5765 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  193-Tot: E=000.5765 | simE=000.5765 | regE=000.0000 | optParE=000.0000 | relF=000.0000 | \u001b[0m\n",
      "\u001b[34m  193-Img: E=000.5765 | simE=000.5765 | regE=000.0000 |\u001b[0m\n",
      "Epoch   194: reducing learning rate of group 0 to 1.9531e-06.\n",
      "Epoch   194: reducing learning rate of group 1 to 1.9531e-06.\n",
      "\u001b[31m  194-Tot: E=000.5765 | simE=000.5765 | regE=000.0000 | optParE=000.0000 | relF=000.0000 | \u001b[0m\n",
      "\u001b[34m  194-Img: E=000.5765 | simE=000.5765 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  195-Tot: E=000.5765 | simE=000.5765 | regE=000.0000 | optParE=000.0000 | relF=000.0000 | \u001b[0m\n",
      "\u001b[34m  195-Img: E=000.5765 | simE=000.5765 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  196-Tot: E=000.5765 | simE=000.5765 | regE=000.0000 | optParE=000.0000 | relF=000.0000 | \u001b[0m\n",
      "\u001b[34m  196-Img: E=000.5765 | simE=000.5765 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  197-Tot: E=000.5765 | simE=000.5765 | regE=000.0000 | optParE=000.0000 | relF=000.0000 | \u001b[0m\n",
      "\u001b[34m  197-Img: E=000.5765 | simE=000.5765 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  198-Tot: E=000.5765 | simE=000.5765 | regE=000.0000 | optParE=000.0000 | relF=000.0000 | \u001b[0m\n",
      "\u001b[34m  198-Img: E=000.5765 | simE=000.5765 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m  199-Tot: E=000.5765 | simE=000.5765 | regE=000.0000 | optParE=000.0000 | relF=000.0000 | \u001b[0m\n",
      "\u001b[34m  199-Img: E=000.5765 | simE=000.5765 | regE=000.0000 |\u001b[0m\n",
      "\u001b[32m-->Elapsed time 0.49161[s]\u001b[0m\n",
      "Writing parameter file = test2d_tst.json\n",
      "Writing parameter file = test2d_tst_with_comments.json\n"
     ]
    }
   ],
   "source": [
    "si.register_images(I0_syn, I1_syn, spacing_syn, model_name='affine_map',\n",
    "                   nr_of_iterations=200,\n",
    "                   use_multi_scale=False,\n",
    "                   visualize_step=None,\n",
    "                   optimizer_name='sgd',\n",
    "                   learning_rate=0.001,\n",
    "                   rel_ftol=1e-7,\n",
    "                   json_config_out_filename=('test2d_tst.json', 'test2d_tst_with_comments.json'),\n",
    "                   params='test2d_tst.json',\n",
    "                   recording_step=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now fetch our registration history to access all intermediate results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "h = si.get_history()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we plot the source/target as well as the warped result. For this, we simply access the last optimization iteration and collect the source/target and warped image which are stored in `h['recording'][-1]` and can be accessed via the keys `iS`, `iT` and `iW`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "plt.subplot(131); show_image(h['recording'][-1]['iS'], 'Source image')\n",
    "plt.subplot(132); show_image(h['recording'][-1]['iT'], 'Target image')\n",
    "plt.subplot(133); show_image(h['recording'][-1]['iW'], 'Warped image')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also show a simple **checkerboard plot** using `mermaid.visualize_registration_results.checkerboard_2d` as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mermaid.visualize_registration_results import checkerboard_2d\n",
    "\n",
    "iT = h['recording'][-1]['iT'][0,0,:,:]\n",
    "iW = h['recording'][-1]['iW'][0,0,:,:]\n",
    "\n",
    "cb_img = checkerboard_2d(iT,iW)\n",
    "plt.imshow(cb_img ,cmap='gray')\n",
    "plt.axis('off')\n",
    "plt.title('Checkerboard (Target vs. Warped)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's also quite simple to plot the corresponding **deformation grid** (i.e., at the final optimization iteration) as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_warped_with_grid(I, phi, title):\n",
    "    plt.imshow(I[0,0,:,:] ,cmap='gray')\n",
    "    plt.contour(phi[0, 0, :, :], \n",
    "            np.linspace(-1, 1, 20),\n",
    "            colors='r', \n",
    "            linestyles='solid',\n",
    "            linewidths=0.5)\n",
    "    plt.contour(phi[0, 1, :, :], \n",
    "            np.linspace(-1, 1, 20),\n",
    "            colors='r', \n",
    "            linestyles='solid',\n",
    "            linewidths=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 130\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.subplot(121); show_warped_with_grid(h['recording'][i]['iW'], \n",
    "                                        h['recording'][i]['phiWarped'], \n",
    "                                        'Deformation (at iteration {})'.format(i))\n",
    "plt.subplot(122); show_warped_with_grid(h['recording'][-1]['iW'], \n",
    "                                        h['recording'][-1]['phiWarped'], \n",
    "                                        'Deformation (at convergence)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Additionally, lets show the energy terms over the optimization iterations ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RRx/lhhtuoKamhtNPP50dO3bwyCOPsGLFCkpLS/nKV74CwLx583jkkUeora3NPKOtrY33v//9NDY2kk6nufnmm3n/+9+ft+72ueeey7p16/jDH/7AHXfcAbg1VWpra1m1ahWf/exnWbt2LR0dHVx++eV85zvf4Sc/+QkHDhzgvPPOo6amhlWrVlFbW8u6deuoqanhtttu45e//CXgVg/80pe+lHn22WefzQsvvMDkyZN5+OGHu62QOFJqjQ+bCDyVTDDzhX+jQ4YAaKrfX/Bn+FkoxR2BBwQ8YKEQiMxlUVtAioHGr339xBNPcPfdd/Pkk0+yYcMGFi1axG233UYikeDTn/40jz32GM899xz19fV9un8kEuGhhx5iw4YNrFq1ii9/+cuZCn/ButvTp0/PXPOZz3yGjRs3snbtWqZMmcINN9wAwPe+9z3WrVvHq6++ytNPP82rr77KF7/4RSZNmsSqVatYtWpV1rPXr1/PPffcw0svvcTq1au56667ePnllwHYunUr1157LW+88QaVlZU5RaqCjJRa48MmAo+3NlEpOtgYXcrCjtW01u+HE+YW9BkZe8IqXgEnUArTCixWBi0U0qom+FDTU6Q8kPi1rx955BE2bdrEWWedBUAqlWLZsmW8+eabnHDCCZl63ldeeSV33nlnr+8vpeSb3/wmzzzzDJqmsX///kxN7mDd7Xxcd911vPOd7+Tiiy8G4IEHHuDOO+/Esizq6urYtGkTCxYs6Pb65557jg984AOZ8rAf/OAHefbZZ7nkkkuYMWMGCxcuBOCMM87otr75SKo1PmwE3F+wS5ZMgg6INx4o+DN8C0UUsQVxLAvFkhoCWdQWkGLg8cVNSskFF1zA7373u6zjfsSaD8MwsnzcRCI3GLjvvvuor69n/fr1mKZJbW1t5rxj1QX/1a9+xe7du7n99tsB2LlzJytXrmTt2rVUVVXxyU9+Mu/zgkjZfS3vYI1wXdfp6Mj///FIqjU+bCyUdMp9h5TlU9zXzQcL/gw/C0Ur4gg8uIjppALNGxwHB40EYUQRz18xeCxdupTnn3+ebdu2ARCPx9myZQuzZ89mx44dmQj1/vvvz1xTW1ubqSG+YcMGdu7cmXPf5uZmxo0bh2marFq1it27d/c4F9+quPfeezMNhVtaWigpKaGiooJDhw7x2GOPZc7PV0cc4O1vfzt/+tOfiMfjtLe389BDD+V0HeqJkVRrfNhE4JbnSxuVk7CkhtN6qODP8CNwzS5eC0JIG1sKdCGxAxG4kDY2GgkRRljFO3/F4DF27Fh+9atfceWVV2YsgptvvplZs2bxs5/9jPe85z3U1NSwePHizDWXXXYZv/nNb1i4cCFnnnkms2bNyrnvxz72MS6++GIWLVrEwoULmT17do9zuf322zl69CjnnXce4Ha+/8UvfsFpp53G3LlzOeGEEzJWD7ge9Xvf+14mTpyY5YOffvrpfPKTn8zM+eqrr+a0007rsR1cV+677z4++9nPcvPNN5NOp/nIRz7Cqaee2u35y5cvZ/PmzSxbtgxwUyrvvfdedF3POi9Ya3zSpEl5a43feOONhas1nq/GbPAfMBVYBWwG3gCu88argSeArd7Xqp7udTz1wHe8vlrKm8rl+sfukYdvmi5f+vGV/b5Xd+xZMVvKm8rlhlsvLPi9C8UrP3inbP32eClvKpfr/vqLzPiLP7tGtn97rDxw04lyzW0fGsIZjl6GUz1wvx624zjys5/9rLztttuGeEYjh+OtNd6XeuC9sVAs4MtSylOApcC1Qog5wNeBv0spZwJ/914PGJZnF2hGmGa9mnCioeDP0HAtFL3II/CkcDNxnHTAQpEONhopLYJuKwtFcWzuuusuFi5cyNy5c2lubs7xdhX9Z8WKFSxcuJB58+YxY8aMrFrjX//61/nWt75VsGf1aKFIKeuAOu/7ViHEZmAy8H7gHd5pvwaeAgZs6d32FjF1M0y7WU0sdaTgz9AyHd+LtzGwJh2SIgwSpNU5T+HYOEIjJcLodvHOX1EcXH/99Vx//fVDPY0BZ6TXGu+TBy6EqAVOA14CxnvijpSyTggxrptrrgGuAZg2bVq/J2p5YqWZEToiY5mY2N7ve3WH7kXgppNg04uP0bLxTyz97P8U/DnHg8Ah5Qt4MF1Q2jjoWFoYo4g/QYx0pJSZru+KoWe41RqXx8hiyUevs1CEEKXAg8CXpJQtfZjQnVLKRVLKRWPHju3T5IL4GRe6GcIuGUeVbC5493h/EdN0ErSuf4AzD97fwxWDg5VO8eaaJwA3CyWtRYDsCNy3UNJ6BMNRAj4URCIRjhw50uf/CRUKcMX7yJEjRCKRXl/TqwhcCGHiivd9Uso/esOHhBATveh7IpCbU1NAHMvtwm6EIojS8ZjCpvHoYarGTizYM3wBD8kUZqLBzfSwLHRjaJN1Xlv1AKe9cC1149dkCThWZ2d6IW0cNGwtjOkU3l5S9MyUKVPYt29fn3c2KhQ+kUiEKVOm9Pr8HpVJuJ8H7wY2SylvCxz6M/AJ4D+9rw/3bap9w077EXgYs8IV7ab6fQUVcN9CCckkkeRRwN1ApBt9r8JWSKx4MwCJ9hY0bFKa27BYBrxuIR0kAluPEpLKAx8KTNPM7G5UKAaD3lgoZwH/BLxTCLHR+3chrnBfIITYClzgvR4wfLvACEWIVrui3dawr6DP8BcxIzJJmeUKeDI59HaEtN3+l46VdndaopGUZlYEjnQ38kg9hNGl4bFCoRiZ9CYL5Tmgu1WZ8ws7nWPMwxdwM0RZzWQAEo11BX2G7lkoYVJUSLf2cFbBqCEiI+C25UbaQieFgciKwN0sFEcPYWAN1VQVCsUgMmx2YvoeuBmOUlblJrzYzQMj4KawMXHriVjpIrAjHFeQbTtNSNpIoZEWZhcBd3DQQTNzO9YrFIoRybCpheJH4KYZpqS0grTUkYnmgj5Dw3GtiQBWKtXN2YOHH4FL23IXWoVGGhNhd1nEFBpSMzGkisAVitHAsBFwbD8CjyA0jQShghdt0nGIi+wC8EURgWc8cMv1wL0IXHOyPXCJhjTCmMpCUShGBcNGwDMReMhNoUsWuGiTY9toQtLRRcDtYhBwr9a3Y1to0kYKHUuYaF0icIkGeghT2AXPkVcoFMXHsBFw7BSOFBiGa3EkRRi9gBG4bbtRa6JrBF4Mi5iOb6GkETiegIeyInCBxBEa6O7vJ1UE81YoFAPLsBLwFAbCqyWcEpGCln31BTypx7LGnWKIwH0LxfE8cASWCKE7uRt5hOEWo0+nimDeCoViQBk2Ai7sFOlA0ky6wDU/bMsVybSe3VHEsopACJ3AIqaUbgSuh7O2zLvphRpCdysVppOqIqFCMdIZXgIuOjNE0lpha37YnmdsGV0j8KHPQvH7XUo7jYaXRqjHCDmBnpheMSs8AbeKYN4KhWJgGVYCbgUicEuPYBZQwKVnoViGG4HHpWtFONbQC6Fwulgomo5txAgHBFzzInDNdOdtKQtFoRjxDBsB15x0VgRuF1jALc9CcUJu3ZMGvcZ9XRQWipcW6OWBS6Fhm6VEZSAC98aF4VkoahFToRjxDCMBT2FlCXhhizZJz0KRnoA3m+OB4ljEFJ6AO7abB47QkWYJUdnFA0dD8wS8KNIfFQrFgDJsBFw46SwBd4wo4QIKuO2JpPAEvCM6wX1OUVgoXgTuWO52f6EhQ6XERBLb8uYtHRyhoZlunrwScIVi5DNsBFx30tgBAZdGhEghBdwTQq1sLC9O/wylS65yn1MMAu5tjXezUNw8cBH2vPp2t7eGu7ipo/sRuLJQFIoRzzAS8FQXAY8SIYl0nILcX/oRuNBZ9s+3MGnmae4Be+gFXPMWMXE6a6FkPim0ufVg/DRCzcsDL4r0R4VCMaAMGwHXZBpb6xRwYcbQhSzYjkM/D1zobqaLEXKFUBaBgAvp+fMBAdejZQAk2l0B17wdmro372JIf1QoFAPLsBFwQ2ZbKITcLe+JeHtB7u8vYgpNB8D0BTxgoaz5ycd5+f9+XZDn9YWgB67hIDUdI+IKeNK3ULyOPLrpC7iKwBWKkc6wEXDdSeNoocxrYbobbpIdbQW5v+NbKL6Ae0LoWyjScTjtyKOktvyjIM/rC1rAA3cXMXXMaDkAqXgrQKZGimG6v6OiSH9UKBQDyrARcIM0TsBC0cOugKc6ChOBZ7I5vGJQmq6TlnpGwNtamzCFjVbgEra9wRdwHAtdSITQCMXcCDzd4UbgwrNWDK9aYzFkzygUioFl+Ai4tHD0zgg8I+CJAlkoTraFApDGQHiFpFob3U7jhayA2Fs0zwP35yI1nXCJG4FbiVbvHM8DN30BVxG4QjHSGUYCnkYGI/CQm0aXShTIQrGzLRSAtDAyEXh702H3ufYQCLjjWyieKAuNSEkFAHaHJ+BejZR83r1CoRiZDBsBN7GQejjz2oi4Am4VKgL3BFzTAxUPMTN1SBItDe5zC7h9v7foXocdv4WaEDrRUlfAZcp9A/MjcL/hhRJwhWLkM3wEvEsEbvoCnowX5P5OHgvFwsiIZqr1iPtcZ/CtiU4LxVtQ1XRinoUik56A+4uYIX/xVVkoCsVIZ/gIeJcI3Iy4HridLEwEnslC0TvfJCxhZLreWG2ugAdLuA4WuuwSgWsaumEQl2FEyv353RopImOhYKnO9ArFSGdYCLh0HMIinSWuoYi7E9EuUATeaaF0RuC2MDO7IJ34Ufe5Bdy+31t8Ac+0UBPuHDtEBJHOjsBDvoVSBBuQFArFwDIsBDzt7SqURmcWSijqWigyVSgBz2OhCCPjgYuORoCCFtDqLTru3DoF3P2zdYgoerrdO8fbYq9ppKSuLBSFYhQwPATc2y4vAmmEkZgbgct0gQTcj3IDUX4wAteTTe5zh0DAfQ/cn4v/JpPUouiW+/Nr2JnI3AqkPyoUipHL8BDwpJf5YXR64JFMBF4YT9qx8lsounSFMJTyBLyABbS6Y+2fbmfH6y9lXhv4Foonyp6Ap7QYpu0JuHS32AOkhJnxyxUKxchlWAi43x5MBATcMEOkpAHpAi0q+pkeWmcaoa2Z6J5ohtPujkddSJID3DB49ss30/CPn2Ze+xaKPxc/0k7rMUKegPtb7MGLwB0l4ArFSGdYCLhfcVAEPHCAhAijWQVKI7TdqFo3OgXcCUTgJXZzZjwZL8zmoe4IYWEmjmReZwRc+haK+2ezAn0x3SwUbxxTWSgKxShgWAi4nXYFXAtE4AAJwogCbW2XGX+5SwTueeOlss1dHAQSBSqglX8eDiYWsVSngBvSj8C9qNqzSiyjhIjTGYFLPzIXnRuQFArFyKVHARdC/FIIcVgI8XpgbIUQYr8QYqP378KBnKSV8gU8OwJPiTCaVaCdkd5GnqAHLjU3Andsm3LZSoPmNjpODaCA27aFJiRldmNmzPfADdnZdAJAmjGieL8bHPAiczuQv65QKEYuvYnAfwW8J8/4f0kpF3r/Hi3stLKxvDRCv9+jT0qE0e3CCLhfC0UPZKE4mokhLVpbGtGFpMkcBwyshZL2/P4qpwnpOEjHISTcNxdD+ht5XAF3QqXEZAfScTCEkxF2K5A9o1AoRi49CriU8hng6CDMpVvsbiLwtBZGL1RtkjwRuKOFMKRFW+MhAOIRt1N9oeqv5CPlCXhUpGhva8YJZLwY0s9C8f5soRIM4ZDwSur6WSi2MNGkEnCFYqRzPB7454UQr3oWS1V3JwkhrhFCrBNCrKuvr+/XgyzPA9e7ROCWFsEsUAQuMwLe6YFL3cQgTXuTW8gqXTLJ/Vqg7fv5sAIt4poO7ycd6KzjWyl+BK6F3Zrg7c3e+6u3iOlmzygLRaEY6fRXwH8OnAgsBOqAH3V3opTyTinlIinlorFjx/brYX5/R93MjsAtPYJZsAg8txohmolJmkSz+8ajVU52nzuAEbgVEOy2Iwcy9hGASfZGHhF2NzO1t7gLnr6FYgsD3W/DplAoRiz9EnAp5SEppS2ldIC7gMWFnVY2fn9HPZQdgdt6BLNAOyP9CFwPRuCaiSktUl4hq3D1NPe5Bdq+nw8r1SnYHU11mU5BACGZnSljeI2NO9oavfl63rgWyqQcKhSKkUu/BFwIMTHw8gPA692dWwj87nODghEAACAASURBVDJGlwjc1qOEC1XeVeaxUIwwBlamEmHpOFfAHc9C2bv1FTasvJhEARc1gxF4qvlQ1mvTt1A8q8RvbJxqc3eJ+vnh7uKrEnCFYqTTmzTC3wEvAicLIfYJIT4F3CqEeE0I8SpwHnD9QE7Sj8CNLhG4Y0QIUyAB9yyH4EYe9BAhYSO9SoSV46e7p3oR+P4nf8bpbc+wf9urhZkDYAcE22mrxw6UhTW9bBShu3+2kBeBp+OugPs7MZWAKxSjA6OnE6SUV+YZvnsA5tItjlfXo6uASyNasOqAfn2ToIWC3+A4fpgWYpRWVLvnpuJIx2Hq4VUAJLwIuBAEBVxrP5zlgfsI4c7Rt5QyJXWDFgrF44G/dfNiGk+4hKUfvXGop6JQjCiGxU5M6Yma2SULBTNGTBy7uFRbSyOrf3EDqeSxFztFnjRCv/phqKOeNlFGOBzFkQKR7mDX5rVMlm56YTrenHvDfhIUcDNxBCdPYwbhzdHvviO9ejD+4qbr3RdPBD4lvRtxZOtQT0OhGHEMDwH3InAz3EXAo5UAvLn2iW6vffPZP7B0391sWfv4sZ/hWShGMNfcE/CSVAPtejlC00gQgnScg2v+mDnNihcyAvc6AEmNaOoIdp7elhkP3G/e4C+qeuNSD2VSDosBHVtt7VcoBoBhIeD4i5ih7Foop1z4OfaJCYx97BoaDuzOe2m6YScAHUf2HfsZ/iKm1vkr8YtnlVtH6TDcHpQJ4dZfGbP/H+wTEwCwOwoXgfsLtg1iDGVWY8YDT8jOHaKZLBRfwP2KjP4Wez2EKYtHwA0l4ArFgDAsBNyPwEPhaNZ4RVUN6ct/Q0x2cPDXV+F4XXWC6M17ALCa9tPW0shLP72K5saG3Ic4NrYUmUwO6CxfWy0bSZlutJ8QETQrztT0LvaPeZs7v46W4/8hPfyIu9EcS6XThONVFUyKzk8GvoWSsZRyLJQQIYpDMP1t/prKS1coCs6wEHDhiZphmDnHZsxdwmvzvsq85EbW/mFlzvFo+34AtNY6tq35G0uOPMzO9XksF8fGRs8a8rfuh4SNHa4A3PorJR11REUKOeYkktJEJgsXgUsvAo9HJlAqOki2ufdO0vnpQ/OE2reUMhUZ/XZwRoiQsAa88URvsLxPECoCVygKz7AQcGknSUozKzoOsviyG3g1cgbzN/2II4eyrZKqVB0AoY5DJA5vB/J71kLa2F1+HcHaK07UzUBJaxEmply7JlIzgzYRQ0u19vMny8WPwNMxt+5KR9MB97UIvHl5vwdfwDUrOwL3s2fSeTJYBhvfAlLFtRSKwjMsBFzYadLHyHgUmkb5+28lJpJsfeIXmXHHthnnHAagJHkYGncB3XjW0sbJEfDOqFdE3XIvaS1CFa5lUj7xROKiBKOAAi79Bs4RN+K3vQyXtAhG4O7vwreU/JK6mS323uJrOlWgMgPHgf8mooprKRSFZ5gIeDI7As1D7SmLeNM4hYk7fp+xDurrdhESNmmpU2k1EGnbC+T3rIVjYYv8FgqAUToGcAXcp2bKSSS0GEa6gALu+f1azH3DcLw3m7QWEHA/jdAMYUuB5hf08j+heG88fiu6ocTPqlG1WRSKwjMsBHzcOz/H3nP/q8fzWudcyXRnH2+tfRKAI/vc3OOd5omMkY2MSbgLmnk9a+nkWiiBrfumJ+C27ka9zZRQXjmGpFFK2C5ccSvpWSi6J+AkXLsnGIEHuwalMDM10f0NPpkIvIfc98HA98BVBK5QFJ5hIeAz5i5hwXmX93jenAs+QbuM0PLiPQC0H9oBwNExZ6ALyVTp+sl5Pes8FooR2DgULvcE3HDH6nU3hTBtlBKx2ziw6y02f28ZRw/v7+NP13UaXsaN94ahJd1PC7bW+WaiaSLzfVoYGF5FRs3bYu9/ckinh17AfU9fL6K0RoVipDAsBLy3lJRVsrniHE5uepp0Kol1ZBcAodolWefl86yFY+VmoZidUW9JpduNxzHcCLw14tbzsswyIk6cA6/+g1PSm9jz6jPH90N4ghcuq/bm6gq4pXe+mWiBCDyNiekX9PIsID/9Md82/MHGr6aoBFyhKDwjSsABjPkfpIJ2Nr/wCEbLHg5TTfmkWVnnmHk8ayGdnAhcDwh4WaVby1x6Ap4snQKAHSqjRLaTbnSzX/xMl/7iR+CxCjcC9/11Rw9YKIHt/kEBF14dl4yAF8EiZmcEriwUhaLQjDgBn332+2mTURIb/0BF23aOmBOomjA9c/wgNYSs3PKvwrFwuixi+uVrHSkoq3QbGksz5h6smOq+DpdTQgKt1bVn/EyX/iLsNI4URL0IPGJ5Aq7lZqGA2/8yJLOzUDQvXz5YV2Wo8Gu5GCoCVygKzogT8Ei0hDcrzmJx06PMsrbQNPVdVNVMJCV1HCk4GD2JiJOnfneeCNzfut8qYp1lZk03Ag/X1AIgIuVoQlLS6vrtfqZLf5FeymS0xC0V68/VMTotlEy+N66A+xUZ/Ropfus5qwgE3M8DVxaKQlF4RpyAA0SXfooGKllz6ndZdtV30XSdI6Kaw2IMqXA1MSc3a0RIG0fkt1BaRXnneV4EXj7xJAA0L197QnIXAJXJA8c1d+GkXAGPuQJe4gm4DAi4v1gJXgSO363efZPxs2fsYkgj9HfRFlFxLYVipNBjPfDhyNy3XQhv201NYKw+Mg2JhhMup0TmtkQT0sbpsohpesWi4npZZqxy1jI275zLjBPnAWDEXAGvwU33G28fRDpOt7tGe0LYKdLCQNN14jJMCXEQXQW8889mC5OITILobPTgv/H4hbGGkoyFogRcoSg4I1LA8zH1U/chhGDzn39EiUhgW1ZW9x2RL43QE/AOoyIzNnvR+bDo/Mxrs6Qy830zJVSIdhoO76NmwrR+zVM4aSzvz5IQYaq9XZ8EBFwPeuCaSUj4lRS9Rg+ehVIUAm4rD1yhGChGpIWSj6qxE6msmYCIuHZIW0tj1nEhnZxFzJDngadDFXRHuLQq8/3O2AIAGva81e95Cjso4AHfO9jMIhDdZ+eHu/PPROBF4IE7XhqhqSJwhaLgjBoB99Girhi3Nx/JHpd2joCbnoBb4Sq6I1LaGYEnJi4GoO3gtn7PTzhpLK9sQCpLwDtL6QbbvjlasAFF5xZ7IG8ziMHGb4enBFyhKDyjTsBNz7PuaD2aNS6kjeyaB64bHGQsYtwp3d4vVl6d+b5y9tsBSHsbiPqD5qSxvS3xqUDdFS0g4EEP3AnUiNEyjR7cc2URbORxbG8jj5CZTT0KhaIwjBoP3McscaPpro2IBU5OForQNMZ96y3GC0F3lJR1Rudjp83mMNWEjm7p90KmJjsj8LTmCrEjRaY7EGT37XT0QKMHzW+15uWvF8FWehno6ZlOJ7PWHRQKxfEx6iJw37NOt2cLeD4LBVyxPJYQR6IlpKVOSupU1UzkUHg6Z7T+g/r/OJF1f72rz/MLRuBpw01ZtNAhEHUH2745eTzwcKQEAGkVgYAH6oCniyCtUaEYSYw6AY/6At6lqYOQNjKPgPeE0DTaRIwjohpN15n6r//LmlO/S6MxlkVrv8Lqn3+mT/fTnTS2F4H7lQ8t9Kzdl0EPXOq5kXk46gl4EUTgTiACt1JJ1v3og6z+7c1DOCOFYuQw6gQ8Vp5dZ9tHk06OB95b4iJGs+nWSqmsmcDiD3yRE7/2HC9VX8LSQ79j04uP9fpemkzjeGJte3VXLGEg9IDX3Z2Ae9eFI952f7/Z8RAi7U4Bt9MpprduwKjbMIQzUihGDqNOwEu9IlFOoouAk99C6Q0Hyk+jcfzSrDHDDLHgUz+jjrFEn/har+0DXVqZCNzxLBQbPVOoCrIjcLRAt3ovAtd0naQ0wRp6AfcXMcEtbxsmheYM/eKqQjESGHUCHgpHSEgTkcjuyiOk0y8LBeDM6+9n2dW5DSeiJWUcfNsKZji72fjIHb26ly7TOJ4o+4WzulooQQ9cGvmLXCVFKNNqbUgJROBWOkVIptCVgCsUBWHUCThAmyhBS2ULuCZtpCj8r2Phuz7KLm0aVW/8uldd4g1pIf2oOuRH4EYmunakyF5UzcpC6XwDShJCFIGAy0ArNTudJCLSKgJXKArEqBTwuFaC3qWpg4aNFIVPcROaxqGTP85J9na2bHiqx/ODEbgIlQJgCy1joXRt+yaCEXigyFVKhDp7ZQ4hQQ/cT93UvcyUdCpZFE0nFIrhyqgU8IRWgtmlJriGMyAROMDc917jtnp79uc9nmtIK5MaqIXdbBJbGGjeImbXei1kCXigU48Io9lFkLYXiMBT7e66gyFd0d502/tYf8e/Dsm0FIqRQI+KJYT4pRDisBDi9cBYtRDiCSHEVu9r93vNi5CkXkrY6hKB9zONsDeUllfxxpgLmNv0NMlEbiXEIAYW0hPrjIBjZMTZIXtTUTACDxa5SmvhTLPjoUQGFzHjrm3ld+epSh0g1rZnSOalUIwEehNy/gp4T5exrwN/l1LOBP7uvR42JKLjqbLqs8Y0HKQ2MAIOEJrzPmIiyda1TxzzPJN0JrPECPsWSvcReHCHZlarNRHGcIogArc7LRK7w4/A05mvfkNmhULRd3oUcCnlM8DRLsPvB37tff9r4NICz2tAsapOYBxHaW/t3MyjSSfTFHggmLX0QlLSoO31Y+eEm4FFTMPbUekIPROB211sHi0YgQcsFEsvEgF37M5vvcwf0xPwkEwRUgKuUPSb/pq+46WUdQDe13GFm9LAEx7vNjmu2/FGZkxnYLJQfGKlFbwVWcDE+ueOeZ5robhRtRF1I3BHGJkaIrKLhaIFGi8Hs1BsLdzZrX4oCWyll0nXtvIj8BBpJeAKxXEw4IuYQohrhBDrhBDr6uvre75gEKic4lYXbN7/ZmbMtVAGttBS+7TzmO7spW53/nrh0nHc5gyegIdjbu1yW+gBCyX7U4JmBjv1BItcRTBlEWR4BBYxRdKLwOmMwMNSCbhC0V/6K+CHhBATAbyvh7s7UUp5p5RykZRy0dixY/v5uMIyccYcAFKHtmTGNAbWQgGYuOgSAPa89Oe8x9NeSp3wBTzqtnJzhNHZab5ryVszaKEEInA9QkgOfQQuAgKueQIekmn3zQqLCEM/R4ViuNJfAf8z8Anv+08ADxdmOoNDtKSMg9RgNO3IjOnYMIAWCsC0mQs4IMYT3vX3vMfTKS8a9cQ6HHMtFCkM9G4WMbVu0gilEck0Ox5KggJueKmbJmnS6RSakERUBK5Q9JvepBH+DngROFkIsU8I8SngP4ELhBBbgQu818OKhvAUytt3Z17rcuAtFKFp7B1zFrPaN+RNJ7T8eileBB4t8SJwzUDrxgPXQ/kXMaUeJlxkFophtQMQEjYd8bbM96rMrELRP3qThXKllHKilNKUUk6RUt4tpTwipTxfSjnT+9o1S6XoaS+tZby1P/N6MCwUgMicdxMTSbas+b+cY2mvh6VvoUQCi5hGNxG4EfDAswTcjBIh1avt+wPBwT1bSXS0I5w0tnTfdMKegAN0BBpq+GKuUCj6xqjciQkgq0+kkjaaGg4CXhbKAOaB+8xa8j6S0qT9jb/lHPMjcM3L7dZ0nbgMI4WeicC7dg0yQ4G+mYHOQcKIoglJKnV8FsWah37Cmod+0qdrHNsm9su3s/HBWxGOTQL3U0LY6RTwYEekZLw15x4KhaJnRq2ARye6qYQHd7obTPVBisCjJWW8Fc2fTugLeHBzToeIIDUD3RvrmoVihF0Bt7sWufIi80THsXd+9kTFG/dR/fqv+nRNa1MD5cShrR4hLTq85syxoIC3NnZ+rwRcoegXo1bAx0ybB0Dr3jeQjoMuJAxCBA4Qn/J2pjv7qD+wK2vcTucKeF14BunKEzvzwLv05/SzUHKKXHlNkNMd7RwPISdBhd03h6yl0U1KElYHwrFICneOMdlZnzwZaGmXOs45KhSjlVEr4BNrZ9MhQ9iHNmP79ToGeBHTZ8y88wHYveHxrHEr7VsonQuT877xNMv+5VYMI78H7lsoOdkpXmf6nmqv9IQpE1TLpj5VDWxvPASAsJNo0iLtCXip6BTwYEu7dIeKwBWK/jBqBVw3DPYZ04g1b8kIuBgECwXghHnLaJVR7B3PZo3bliuSWiAC99E9AZddLBQz7HWu70bA08nji24jMoEuJI31B3p9TaKlwZ2DnURIG0uYpGX2vO14Zz32dEItYioU/WHUCjhAU+lJTEjsxLHdeh2DsYgJ7pvH9tipTGpclzVu54nAM9fo+RcxQ74H3nWDj9cMIn2cEXjY2wzUdHhvr69Jt/oCnkJzLByhkyb7040MtLSzEspCUSj6w6gWcHvsbMZxNBNdikEScIDE5GVMlQc4vH9n53y8jBHdzI3ANV3HliInDzzkWShd67hkBDx5fAIe8TYDtTfs6/U1dvsRdw5Owm3SLAysLp9uZKClnX2cnxIUitHKqBbw2OT5ABzastYdGEQBr5n/LgD2BHzwjIWSR8DBbW7ctfGypuukpJ4TgfvWin0cAp5KJjCF++kk2bg/7zlbNz5LKpmdqijj7qKn7qQQ0m0WbXWJwP1t9QBOHgGXjsOaB/+LjnbljysU3TGqBXz8zNMASOx71R0YJA8cYMbcpXTIENbe9Zkxx7NQDDPXQgG3ubHM8yezMHI3+ES8hsjHIeDBDTZOS13O8Z1vvMTMP13Exr/8LGtcS7gpgoaTQu9OwNOBe6dyBXzX5rUsfm0Fb6z6Xb/nr1CMdEa1gI+bNIMWYkSOuGVlhT44WSjg+uB7zVpKmzsrEzqWW6VP70bAbbQcDxwgJczc7JSw1xA53ZFzfm8JbrDR2w/lHD/4wm8BkHWvZD876Qu4m4XiCBPL6zfqeLsyjXQgsk7lzrHDyxO32470e/4KxUhnVAu40DT2m7XMaN/oDQxeBA7QVDaLycntme3u0vIj8G4sFJE/Ak+TR8D9ZhB5xLG3JDs6o+RwR3bBSek4TD3g7iYta92edSycchcoTZlC8yJw24vA23E9+1CgJ6lM50bgfpqhE2/MOaZQKFxGtYADNJ9wESlCHBDjqZxx2qA+W46fRxWt1Ne5RbUcr/2YHqhvEsRGz9t0Ip0nAg95Foo8DgH3N9g4UlCSasg6tu3V55kiD9JCCRNTu7JqrkStTgHXpY3UDCzhpkG2C7e+S9h2721LgUjn2jx+brhIKAFXKLpj1Av40iv/nXErdjLppi2cvOidg/rssukLAah7y11Eld4iphnqzkLJL+CWMHPGw1E3ApfpDra/tppEP3Y7+iJ6WIyh0s62Mo688BvSUueNqVdm3oTW/PhKtmx4mlLHb52WQsNCCh3bs1DimjuviLetvkWUouURcD9PXE825RxTKBQuo17Ah5LJs88EIL7X9ZB9Ae9uEdPJk4UCYOexUCKegNN2mKl/uJCND/6wz/Pzc8gbwlOols2Z3Zg733iJMw49yMaq5ZTOfDsA2/96G4ubHuXo6nspl67wh/EWMTUzI+AJ3Y3AS2QcWwriIoZm5xbckn7zh1RzzjGFQuGiBHwIqaiqoY6xhBo2uQOehWJ0F4ELjXx/Mksz81QpDGNJjWjzVkLCxjj8ep/nZyVcIY6XTEUTkqOH92NbFumHPk+rKGHmx3/MxJPcTxGn7r8fgOqjG4kIt4RsSKbQpR+BuxZKynRrnJfIdlKYpEQU3cqTKeM9O5xuyT2mUCgAGLy0C0VeDsZmUtO+FQDZ0Ygltc7ouQvOMSwUM4+wJwkxNuH66xXtO3OO94SdcoXVqZwBR6Hp0B6O7NvCKdYW1i74D86smYB0HJopoUK4lshJ6a0g4KioZCyNbpNmzcDx6sxYhhuBh4RNMxFSWgTDzuPTp9xFzpijBFyh6A4VgQ8xyepTmGLvI9HRTrh5B3XahO49cNGNhaKFcmqkACRFiAmOmz0y2dqbKRnQW/wNNrGppwLQvGsjTW8+DcBJ53wY8DN5ZgCwMbYMTUgAmowawK2lIjUDx4vA7VBZ5v4pQqT1CKaT25FHS7kReKmjNvIoFN2hBHyIMSfOQReS/dteozK+h6ORqd2eezQ2g2T5jJxxW+RaKAApwhlBjYkkh/Zt69PcHC8CnzrvLBqoRNu5iljdGnZrU6kaOzFzXkv1fBopJ3LuDZmxtpDbwDpGAoSBrXnVFM1SLOnONS1CpPUYppMbgfv9M8tl25B1FVIoih1loQwx1TNOhbXQuPNl5toHOFy+tNtzT//KX/KO2ws/zpH2Rk7sMp7SQuC4aYCakBze8SoTp5/c45xsy0IIgfQEPFpazmsVS5jZ/DwaDpur38X0wPkL/ukW2lu+Tm3VWKxHNQzhkIpNgDjoQiJ1s7PfqBkhjYFByk1/1COEndxFTL9/piEcWlubKKuo7nHeCsVoQwn4EDP5xPluqdVtfycqUoiamX2+x+nv+WTecb8O9zZzJrOsLXTsfwP4UI/3e2Plu+komQLRahwpCIejiJPOp3K928dTr31b1vmx0gpipRUA7NSnMsPZjVM2EfzUcaFnLBSMCGlhEiWFJULYRpRwns70wf6ZrUcPKwFXKPKgLJQhJhSOsF+fxKyW5wEomdRzhNxbfAFvrJjDUcrRjmzp1XWTEtsoa9mGSHfQQRihaZyw5KLMNvhJC7rPl28oc+dvVEzOjLkRuCvgwoyQwv3eEiEcI0aYXA887MRJSW/3ZnN9r+atUIw2lIAXAUdjJ7g9JIHxM+YX7L6W7gq4UzGNutB0xrRsZu2f/puDe7v3wh3bplK2UGI3Iaw4Ce9NoHrcZLYbJ3KIMUyc1v2nhJJln2L1hI9hxCo6BzUDqXsCbkSwfAHXQkgzRlTmCnjEiXNYc310v0GEQqHIRgl4EZCsdqPWdhmhZsK0gt3X1lzxDY2ZTlvZiZxo7+DMjd8kdc8lNB/Njmp3b15Pw8E9NB05iCEcyp0WNKsj088SgIt/TMPy27ObJ3dhztL3sPQzP0P3OgIBroB7EbgWipL2M1K0EIRimMLOKUkbI05jaAIAqVZV0EqhyIcS8CIgPGkuAHXG5GOKY1+xvQi8ZPwMJiy/nhenf4a1C7/PBPsge/7nQ52diByH2P2XsfN3/0Zzg9vcokK2YqTbSYnOuiwzF57D3Ldd2KtndyfgeiiaqYtiayGE6dZsCZaulY5DqeygI+baMFZ735oq56O5sYGXbv8XVV9cMaJQAl4EjJnh7mZsjk3v4cy+Yeuu+NZMmcn0kxey7J9v4cxLr+Xled9kfvJlNvz1LgAO7dvOWBopje+lzRNwTUhKk4dIafkLa/WEEeq8TuhmxkLRAgLu6GFEyN20FCxdG29vQRMSu3yKe178+AV8x9q/saThQbatf/K476VQFAtKwIuAySfMoYFK0hPPKOh9HSNKUppUj52cNX7mB69nm34iUzasJNHRTt3mFwCoSh8m2dTZuGGMfZh0PwXcL2cLgG6CF4EboahrnQCOFkYP+wLeQiLexot3fYmjB93do1rpWOIyjOg4/oJWtrc1v+NI73t7KhTFjhLwIsAwQ5T82+ssvuIbBb1v9dlX88r8b6LpuW3YEu/8DhOoZ+ODPySx2+0KVCOPYDV3dp+vpgVL76eAhwMRuGaA7oq2EYpm6qI4egg94m6tT8bbePPFR1i2/x72PXsfAHqkjBZRRrRlOzv/41Q2rf5bv+YCnQJuN+VvDadQDEdUHniREC0p6/mkPjLztLfDaW/Pe2zeWRez6en5TN5+P03eYqEhnJyiV7YezXd5j/gdgcC1UPAsFCMSczcYAVIPE6lwM03aj+wnccAt6hVpeM09N1ZBu17GqR1rAHjxrWdh6Xv6NR/pCbjW1vkJI5VMIITotnSBQlHsqAh8FNN2yoeZKg9wSmIjhxgDwIS2NzL519B/AfcbSgCgG66I4wq7v61eGmEmzXJto/i+19COuEW9JsffdO8RqyChl2duo7Xm9uXsLdIrjhWOd7aGe+32j7D5tt4tyioUxYgS8FHMnPP/ibgMYwiHXWPPA2CKPMg+fUrmHMfon4USFHBNNzMWihmO4XgROEaEiqoaDlKD2bCJ8ja3YuI43EXLcEkFSdPNJ28hhtmlrVtfEJ6Al6XcezQfrWd+yzPUJJUnrhi+HJeACyF2CSFeE0JsFEKsK9SkFINDaXkVb1S+A4CShZdmxlvCE2iVbuQtzfylbXsi3CUCD4+ZRruMUFY9DscTc+GlOR6KnsiYtq1MsvZk3SNSWoF2+sd4cca17A6fTEmy/wKueQJeZbubgt56+n8JCZsS2XasyxSKoqYQEfh5UsqFUspFBbiXYpAZd+E3eanmg8xe/G6accU6FRlDi+ZaF9LspwfuNZQANwJf+K6PwlfeoqyiGulH4F7noXjVbKbbeyinnYOMzdwjWlrJwvM/wrJPfJ9EZDyVliu+Lz3wQw7ty26k3BN+dcNqWkgm4kTeehiAMhnvc5ldhaJYUBbKKGf6yQtZ8vl7MMwQDfo4AOzYONr0SoDMRpv+4Nc80fQQQtMoKXPvKf0I3LNnzEnzMmVvd9eck7m+pLwq871VMp4xspG63W+xZNPN7Pjrj/s0FyPQ9WfPm+s5pWMDLcTQhKS1RTVOVgxPjlfAJfC4EGK9EOKafCcIIa4RQqwTQqyrr1dFiYqZ1rCbjSJKx9FhuuIpQv0X8KTwrZLsZCfpWSea6W00OvH0zLHY/IsBSEkjy4bRKiZhCId9L7sbcUoa3+jTXEK224MT4Ogzd2IKm81jlgPQ3qRqrSiGJ8cr4GdJKU8H3gtcK4TIyVmTUt4ppVwkpVw0duzY3DsoioZkzG3SYFaMJx32BDzcPw8c3I47AJphZo13RuCukE8+cT4padAuI8xc/G4cKWgX2daNWeltRtr5FABTE1v71OghbMep09w3qPlH/o8GKjFnvQuAuCqWpRimHJeASykPeF8PAw8BiwsxKcXQIL2t69GqSdgRt/62fhwReLqbCNzPSNG87fZmKMweb89vhQAAERNJREFUYzr7zWlEoiUc1MbSIbKfWzrW7VRU27wWgCpaOLR/R84zjx7ez0v335Ij7mEZpyFaC7jdiXZUnU24zE2dTLQe/1Z9hWIo6LeACyFKhBBl/vfAcqDvrc8VRUNs6qmkpc6YqbOgxO1pqR9HBO4LuG6Esg94r/VAvRTnff+FuOg2AOojtbTpFVmXVIxzBXwsjbR5GTJ1m1fnPHPLo//Nks3fp27P1qzxqOwgGR1PXLpRv3nKe4mUuW9SqTYl4IrhyfHsxBwPPCSE8O/zWyll//c6K4ac+ed+kMa5yxg/bjK7S127y4gch4BrYXByI3DfOtEDGS6zTj838/24j9xOKpHdJ3PM+KmZ1nCbK8/h9KYnSOx9Gfh41nmhBjeGaDtaB7WdzTFKZAdOqJQj2hh0p55Zyy6itdFdk7Hae17ETCUTmGaooNUiFYrjpd8CLqXcAZxawLkohhihaVSPc73m2Fi3MmJJ1fh+38/qJgL3BdwI5U9RzNe30zBDNIgKamjCnrCQva2biTa8zt5trzFuyomZBc9x7W7XoXjjwcy1qWSCkLAgVEpdxUIOSoszyyqR0s18ceLHFnArnaLpB6ew85TPsOSKr/XmR1coBgVVC0WRl7lvu4itpY8wc+6Sft/Drzqo6dmLmJkIPNS3XZ6Neg01dhMlk+fRUDebRS1PwL1n8+Lkf2bZv/6YtpZGpkh3u32qpXPTT7y1iRAgwmUs/tLvMuMlpRVYUkP2UO1w//bXmc5Rdjb0riWdQjFYqM+DirwITWPmwnN6PvEY+JUMDSO/gBvhvm0Sag+7ts74ExcQWfIJNpScwy5tKpPqngBg7+Y1mXPt1s6U1XhbM+BWN8yah6bRKkrRks3HfG79drdao97DeQrFYKMEXDFg+DVPNDPbQpl62gWsK38XE2fM6dP9EmW1HKGCsROnM++sizn93x7h4MyPMt3Zx95tr9G8wxVaRwpEe6eAJ9vdCFuLlOfcs02UYqRajvnc9H6vOmJadfNRFBfKQlEMGI63YUfvYqFMmDaTCTc82Of7zf3o92k9+qWshcRpyy6Dt25h/0t/RKt/k6OUkxJh9ERnH81kuyvQZjS3ZG+HXoqZbuHAzjfZvepuzMbtxN72r8xZ9l462luJREuINbrVEcPWsYVeoRhslIArBoyMgJtmD2f2jrKKasoqqrPGJtWezE5tOmN2/oWI3c7+8EmE7TbCyc7UwFTcFd5QLDcCTxhlRKwWDj74byxpfZY2ESXyt6d5+bmlzG97gZdO/jIzOrYBELVVBK4oLpSFohgwpFfrRDcKI+DdcXDS+cy0tjJVHqB92jvoMCuJpTszS6wOT8BLKnKuTZvlRO02xrVvZWPpOcgvvsq28Cmc0vYSDaKaWVv+h/G40XzMaR/Qn0Oh6CtKwBUDhswsYoZ6OPP4WHDFTbx23j00fOZ1ln7sJlLhMZTZnZkllteNJ5JHwK1QBdXOUSY5B0mOOYWK6rGc/NWnsL68lUPn/ifVuOK/U6ulTJWeVRQZSsAVA4eXbdK1FkqhKSmrZP65H6Rmgrtb04qOoVK2ZLbTO56AR0tzBdwJV1AiEmhCEpmyAADdMCgtr2LBuZexS3PveXjMIqIiRTIRz7mHQjFUKAFXDBha2Tg6ZIhwpH81xfuLKKkhLNK0tbpRuEy6Ah4ryxVwEekcG3fSGdnHNI2Wc1awpup9UDMLgNamIygUxYIScMWAsfCSz9PwT//I7s4zCOilbl3z5voD7kCynZTU885Di7lVF9tlhInTZ+UcX3De5Sy+7rfoMbeWeXuzEnBF8aAEXDFghCMxpp40f9CfG6pwBbzN206vpduIi/yfAowSV8D3mjPQdL3be5qlbvZLRwFKz6ZTSVb/99Xs39G3muYKRVeUgCtGHLEqt+53R0DAO8gv4CFPmJsrcqPvIGGvcmGy7fi792xZ+yRL63/P3hf/cNz3UoxulIArRhxlY9zGFGlvO72Rbieh5bdxIuVuTXDGzT3mPaNe7fB0AUrPtrz5D++bumOel0yofp2KY6MEXDHiqKxxBdzxBNy020l2I+C1cxazesqnOPn8TxzzniUVroDbPVQu7A0VB9065kb8cLfnOLZN/S2n8dJvvnncz1OMXNROTMWIIxItcZs+xF2/OmTHSRi52+jBLVO79OrberxnWaUr4E4PlQt7oqO9lZNSm0FALNm9gG975TlmyYMcblA+uaJ7VASuGJEcMiYwruEl6na/RW16O/GyE47rfuFIjA4ZQhyngG/f8A9CwqaRMsrT3S+IHtn4VwBKk4eO63mKkY0ScMWIpHHhZznB2UXq1x8EoPbi42/E0JvSsz3e481VWFJja/U7qHaOdtuYuerAMwBUW91H6QqFEnDFiOT0C69mu34C0519vFxzMROmzTzue8a1kuMqKSsdhykH/o+3wnNxxswkJpK0NOcuijYfOcTM9Ju0ywg1NKndn4puUQKuGJFouk7yXd9npzad6Zd+qyD37NDLCKf7X1L2rfX/cAtunfJhjMpJADQd3J1z3tYXH0YXktdr3g1Aw4HccxQKUAKuGMHMWfZeZnz7VSZMPakg90sa5YTtNvZue42Wfmypb179G+IyzJzz/4lY9RQAWur35Zxnvv4Ah6kmOv/9ADTV7Ti+iStGLErAFYpeYplljLUPMub/nc+2uz6Zc3zbK89zYNdbea/taG/llCNP8EbluZSWV1ExfhoAiaN7s847uGcr8zvWsWPKB6ia7L7xxBtUBK7IjxJwhaKX2OEKKmgnJpKc3v4Muze7LdysdIrVv7iBE/74Ppru+5e81776y2spJ07ZWdcAUO0JuNV8IOu8nU/cAcD0Cz5NzSQ3c8ZuzI3SFQpQAq5Q9Bon4ha0eqn6EuIyzJG/ruCVfzzArv9cytJ9d3NQG8vJqTdoajiYdd26P9/BkiMP8+LEq5i9+AIAoiVltFCC1tp57p4tG5m59w+8Hl3E/2/v/mOjvus4jj9fPdpCoaVA+RVAfizdJo5NSkOYYzVmRgs6mNMYiD+Yc5ItW3QxS4YhUeJf4k9cQlw2QKfitkwldMnULdMMo8LWIoUiP1oYC7gOCnUOLFCgb//4fm+5tncFetfv3Rfej6Tp9z58774v3t/vvfu9733vvpOn38SIkeX8h3J02hu4S88buHNXaFR1HXtL5vCh+55g96R7qTmzjdu2fY3Rlzppmr+OM3dvICGj7e9bgODTlP/Y+Bg1TavYVzyb2q/8sNfjdRaNo+RscJ53y99eZOzmehL0MKL+O+/PcyoxgeFdA3/k3l2//JOYzl2hOXVLoS54Y/HWL62l6bVaSisnMmPOQuZVjKHn0iVONlSig38EHmLHhq9ze/uveaOynltWbqC4pLTX450urqLsfAfvHG1jyisPcTJRxfD7tlCdcsrj6dKJVJ7rfZjFuSRv4M4NQtmo0cz71AO9xooSCQ6PuYMPdv6Z7esf4PaOF9gx7h7mP/xzVNT/xe654ROofvdVTm1aTIldILF8c7/z1bvLJjGuaxdHW5sZPX4qFeFH+p0DP4TiXE6VzllCuc4y/8RvaSy/i3kPPp22eQOM+sj9HCibS1eigtaFP2Fa9W395rGKqVTQxbTNdXSvq6Hlr1uH+r/gYkRmFtnCamtrrbGxMbLlORc16+nhrQM7qZpyA6MqxmT9eEdbmzn+4ne5OGkuk9ueZXrPMQ4Ou5HOsXOx4jJUXAYlI1BxGYnSMpQoBmmAR0z/xyTjXfr8g1JuW8pjaYBlSkWpN65godemabcsZNzEqYO6r6QmM6vtN+4N3Ll4+N/pd9mzdR1VbzYw6eLbjOAcCUX3/HXZ2f3Rjdz6sc8N6r6ZGrgfA3cuJkaWV7Lgi2uANUCwt999oZuzXWfoPnuG811nuHThfMb7G+mbfcadOOvpM1+G+/SbL/XxLP14hDuOhWLGzIEvGjIYWTVwSfXAT4EEsMHMvpeTVM65y1JRESWlwykpHQ5jqvIdx+XBoN/ElJQA1gOLgNnAckmzcxXMOefcwLLZA58PtJnZYQBJzwFLgX/lIliqta+vZX/n/lw/rHPORebmsTfz+Pzsv5c+VTanEU4BUr+J51g41ouklZIaJTV2dHRksTjnnHOpstkDT3cOUL93JszsKeApCM5CGcyCcv1XyznnrgXZ7IEfA6al3J4K+Gd+nXMuItk08DeAakkzJZUAy4CG3MRyzjl3OYM+hGJmFyU9AvyJ4DTCTWa2N2fJnHPODSir88DN7CXgpRxlcc45dxX8y6yccy6mvIE751xMeQN3zrmY8gbunHMxFenXyUrqAN4a5N2rgJM5jJMrhZoLCjeb57o6hZoLCjfbtZZrupmN7zsYaQPPhqTGdN+Hm2+FmgsKN5vnujqFmgsKN9v1kssPoTjnXEx5A3fOuZiKUwN/Kt8BMijUXFC42TzX1SnUXFC42a6LXLE5Bu6cc663OO2BO+ecS+EN3DnnYioWDVxSvaQDktokrcpjjmmS/iJpn6S9kr4Rjq+R9G9Ju8KfxXnIdkTSnnD5jeHYWEmvSGoNf4+JONNNKTXZJek9SY/mq16SNkk6IaklZSxtjRR4ItzmdkuqiTjXDyTtD5e9RVJlOD5D0tmU2j0Zca6M607St8J6HZD0yYhzPZ+S6YikXeF4lPXK1B+Gbhszs4L+Ifiq2kPALKAEaAZm5ynLZKAmnC4HDhJc0HkN8Fie63QEqOoz9n1gVTi9Clib5/X4DjA9X/UC6oAaoOVyNQIWA38guPLUAmBHxLk+AQwLp9em5JqROl8e6pV23YXPg2agFJgZPmcTUeXq8+8/Ar6dh3pl6g9Dto3FYQ/8/Ysnm1k3kLx4cuTMrN3MdobTp4F9pLkOaAFZCjwTTj8D3JPHLHcBh8xssJ/EzZqZbQM6+wxnqtFS4JcW2A5USpocVS4ze9nMLoY3txNc8SpSGeqVyVLgOTM7b2ZvAm0Ez91Ic0kS8Hng2aFY9kAG6A9Dto3FoYFf0cWToyZpBjAX2BEOPRK+DNoU9aGKkAEvS2qStDIcm2hm7RBsXMCEPORKWkbvJ1W+65WUqUaFtN3dT7CnljRT0j8lvSbpzjzkSbfuCqVedwLHzaw1ZSzyevXpD0O2jcWhgV/RxZOjJGkU8DvgUTN7D/gZcAPwYaCd4CVc1O4wsxpgEfCwpLo8ZEhLwSX3lgAvhEOFUK/LKYjtTtJq4CKwORxqBz5gZnOBbwK/kVQRYaRM664g6gUsp/eOQuT1StMfMs6aZuyqahaHBl5QF0+WVEywcjab2e8BzOy4mV0ysx7gaYbopeNAzOzt8PcJYEuY4XjyJVn4+0TUuUKLgJ1mdjzMmPd6pchUo7xvd5JWAJ8GvmDhQdPwEMWpcLqJ4FjzjVFlGmDdFUK9hgH3As8nx6KuV7r+wBBuY3Fo4AVz8eTw+NpGYJ+Z/ThlPPW41WeAlr73HeJcIyWVJ6cJ3gBrIajTinC2FcDWKHOl6LVXlO969ZGpRg3Al8MzBRYA/02+DI6CpHrgcWCJmXWljI+XlAinZwHVwOEIc2Vadw3AMkmlkmaGuV6PKlfo48B+MzuWHIiyXpn6A0O5jUXx7mwO3t1dTPCO7iFgdR5zLCR4ibMb2BX+LAZ+BewJxxuAyRHnmkVwBkAzsDdZI2Ac8CrQGv4em4ealQGngNEpY3mpF8EfkXbgAsHez1cz1Yjg5e36cJvbA9RGnKuN4Phocjt7Mpz3s+E6bgZ2AndHnCvjugNWh/U6ACyKMlc4/gvgwT7zRlmvTP1hyLYx/yi9c87FVBwOoTjnnEvDG7hzzsWUN3DnnIspb+DOORdT3sCdcy6mvIE751xMeQN3zrmY+j+Os7yQfcAOhgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "e_p, = plt.plot(h['energy'], label='energy')\n",
    "s_p, = plt.plot(h['similarity_energy'], label='similarity_energy')\n",
    "r_p, = plt.plot(h['regularization_energy'], label='regularization_energy')\n",
    "plt.legend(handles=[e_p,s_p,r_p])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using mermaids visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ww2aM2+nmc2xsjO6++2569NFHqbq6mt797nefdj6VUvTjH/+YduzY8bzb9WIGz9e9995Lr3rVq2hwcJB+/etf0+joKO3cuXPdMWPI+SU6NcmRyWTacAw3Soq0Xnv5HjnPPMdERF/72teovr6eDh48SOVyGZnx1pvn50s/ng+2VKc7MzNDbrebrrvuOrrtttvoiSeeoN7eXhofH6eRkREiIrrvvvto3759RFTRDz3++ONERPTjH/943edeccUVdODAAQzw0tIS7dixgyKRCAatUChsSjLpeDxO1dXV5Ha7aWhoiP7v//6PiIhe/vKX00MPPUTLy8tULBa19l5xxRVawu6nnnrqBbfjxQafz6dxofF4nOrq6shms9Gvf/1rLXOYxPMZt9XveiFYWVkhj8dDfr+f5ufnkQint7eXTp48CU6J7Q9ERK9//evpG9/4Bjbsk08+uSlteTHh8ssvp7vvvpsuv/xyuuyyy+jAgQO0Z88eMplM647ZeuCx+93vfkd+v5/8fv+6Y3j55ZfT/fffT0RER44coUOHDm1Kf+LxODU2NpLZbKb77rsP6UkvvfRS+vGPf0zlcpnm5+fpN7/5DRHRltGPtbClRPfw4cP08pe/nPbs2UNf+MIX6NOf/jQ5nU6699576W1vexsNDAyQ2WymD3zgA0REdNddd9FHPvIRuuyyyza0gr7vfe+j1tZWGhwcpN27d9MPfvADstvt9MADD9Dtt99Ou3fvpj179qzrZvRc8IY3vIGKxSINDg7SnXfeCTG5ubmZ7rjjDnrFK15Br33ta2nXrl1IaP31r3+dHnvsMRocHKRdu3bRgQMHXnA7XmwIBoN0ySWXUH9/P3384x+nd73rXfTYY4/Ry172Mrr//vupt7d3zfuez7i96U1vop/85CdrGtKeK3bv3k0XXHAB9fX10Y033kiXXHIJEVWSsd9zzz30hje8gS699FKqr69Hu+68804qFAo0ODhI/f39dOedd76gNrwYcdlll9Hs7Cy98pWvpPr6enI6nXTZZZcR0fpjth6qq6vpVa96FX3gAx+g73znO0S0/hjefPPNlEwmaXBwkL785S/Ty1/+8k3pzy233ELf+9736OKLL6bh4WFw4295y1uopaWF+vv76aabbqJXvOIV5Pf7t4x+rIkt0RS/RJBIJJRSFSPSVVddpf793//9LLfo3MCLddy4XeVyWd18883qq1/96llu0bkHafB6sYLnORqNqs7OTjU7O3tG3284vL4AfPazn6UHH3yQstksXXHFFXT11Vef7SadE3ixjtu3v/1t+t73vkf5fJ4uuOACuummm852kwxsAa666iqKxWKUz+fpzjvvXLP001binAuOMGDAgIFzGS+5MGADBgwYOJswiK4BAwYMnEEYRNeAAQMGziA2NKSx03FnZyfFYjEiIlpYWCCv10tElUiwyclJIiLq6OiAi83IyAhVVVURUcVXl6hSPpsV1tFolAKBABERzc3NoTBia2srTU1N4Xn8/eLiIhFVIlHYKbu+vh55GvL5PJ5dU1MDX0BZdJHzO3i9XjhWOxwO3JfP51FOO5PJoL9NTU1ERDQ/P49379mzB/6ELS0tcOZPpVJ4djabxXhwGxYWFuBKJUt353I5CgaDRER09OhRREUtLi6irPlmqt65T5lMhlKpFBFV3Hw4Aicej6PN0nUvFArBd7a1tZWIKvMnHeX5+o6ODkQMjo2Nof1KKYw5rxGr1Yr5ISIEyAwPDyNyMRKJUDweJ6LKmHO7+bnlcpnq6+uJqDK2XFizurqaWlpaiKgyh+l0moieKeduMpmouroa7eDy7+xzzO/mz/l8Hv3lOXS73Yg2tFqteHdXVxfGQI5TVVUV/I6XlpZoM8FjqpRCH51OJxUKBbyP96/b7cY85/N5fOYxdbvdqJ2XSCQw/oVCAWve7/djbJRS6CM/q1gsYj5tNhtoSi6Xg+8sEa0Z7MDP4Dnj33lM7XY72e123M/P4/d5PB4kV8rlcmh/Op3G+6xWK9rv9XrxDN7/NpsNazqXy2GeA4EAqmSbTCa4pOVyObSXIzJXY0Oiu2vXLiIiSiaTGHy73Y4JDIfDFAqFiKiyMPn7jo4OECUOw4zH4zQ6OorvmFhv374djvTJZBKDuLi4iOQXTKDr6+uxWMfGxmj37t1ERHTs2DEM3OLiIhbeysoK3s8DMTIyAsI3PDxMkUiEiCphojyIDocDE8uRLO3t7SCAi4uLVFdXR0SVQ4UXsd1uxwFRKpWwodrb24mo4uQvNz0HiPT29mpEngmQxWKhPXv2bDBDzw+8mGtra9G/5eVltGHXrl24plwuo5pvMBikrq4uIiIcjjU1NZgTv9+PcXniiSfQ9o6ODoxLMBhEIhE+kMvlMjZxR0cHCF8wGMTaaGxsxPw4nU68k++bnZ3FwSXf0dbWhrW4fft29IuJQzKZxNqZmZnBeonFYtg0mUwGm6qxsRFzyGM3Pz+Ptep0OrFxo9Eo1tfAwADGbGlpSTtkNhO8FiURLZVKGkFigmOxWLDfyuWyNud8n/zMxNjhcICYZbNZLaKMA5b4d4fDAaIl32Gz2fBupRRoh1IK1/MelIRREtdisYjPZrP5FKJrt9tBwB0OB8bGbDZj/svlsnYfv5sPrEKhoM0VX7s6ipGfVywWT8sgbei9wAO3fft2NPjQoUPgVLLZLLiE2dlZLEy/34/BZywsLOB59fX16Nzo6Cg++/1+LOjt27eDSDOn4nK5EDFUW1sLIhgIBHAChcNhTGZtbS0q2fIzisUiCEAul0NY4sTEBDZwLpdDf3kCC4UCCIrJZNIWkuRgmJCazWZqa2sjIsLGi8ViWDypVAqHg+Se+vr6QGhk1jXesJuB7du3E5HOvTQ1NaHPFosFC81ut4OA+f1+cJjcLr/fjzwKY2NjIHylUgmc/szMDDjW6elpcKT87lKpBKLl8XhwyPLBTFQhfPy8pqYmbF4mxNFoFO9eWloCM1Aul/G88fFxam5uJiLCGO/YsQN9tVqtWF9VVVU4NHft2oX3ZDIZEHx+ViKRANerlML7JHEaHh4GQZ+fn6e5uTlcv5ngd6ysrGDdEZFGqLh9gUAAeyWbzWI+eC3abDbs73Q6DcLo8/kwZisrKzh8bDYb+svfcV4Eosr653XT3NyMd6dSKY3LZkLJNKRQKGBtEhHGP5/Pa0R+9Vj6/X4QT24DP4/nM5fL4T6fz4d3M8GPxWKgSTKsXP5dLpexfk0mE+5liWc1DJ2uAQMGDJxBbKheYE5tfn4eJ1RPTw/EUI/HA+6wvb0dp4fU47BaYmFhAdxOuVzGtQ0NDTgd7XY7TrxIJALdJnOVkUgEn9PptCaeMFdss9lwEtrtdojDUoTkk7yrqwsipDzx0uk0uBj+3ev1QuwNBAI46Z1OJziyQCCA8ZA6Zz5tpQ7L5XKBQ8xms7gmHA5DXBseHobudDPBc7O8vIzPFouFnn76aSIi5JogqkgwrOudmZmBqoRPcZfLBa6tvr5ekxD4s9VqpaGhISKqzDdzYFKyYKklmUxq3DLPcU1NDdbG6OgouDUeK5fLBQ56dTY3lhLa29vxmdf2zMwM7isWi3iH2+3GfB0/fhxSnMvlwtyzVFMul6mvr4+IKvkipJjO7evv7wfnzBmutgLMlZnNZnD72WwWnJjcm6s/897ieSmXy5gXr9eLMVheXtbEc77PYrGAG2auWeo7lVKQ7sxmM8YpkUjgGofDgT5wm3O5nCbiM3frcDiwTqXKgO+Px+P4jq8jqqwrfka5XMYYKKU0iYDHgteg5Oq9Xi+uyWaz6LfH4zmFI16NDYkubxqpHF5YWICYPTIyAkKVy+UgttfX12PhMzFsbW0FEZKNrK6uBhEvFovoSCqVwoDx+zwej2b4YUK6vLyMwXK73bg+nU5j8nlhSNFI6nebm5tBGJqbm0G4uW35fJ46OzuJiGhoaAhEuVAoQATL5/NYpJFIBJuZiXKxWKTu7m6Ml9ycPB6BQADiycTExLoiyguBNFbyeM/Pz8PAZrPZMEYdHR108OBBIqqI9SyW8yKuqamhcDhMRJXNI3VikkDzQhwfHwfB5D5bLBYQiG3bttETTzxBRJVDk783m82Yy+rqaowzz0ltbS3GU4qr2WwWG9pqteKAl0Yh3nREBBVGsVjE+gsGg5phmN/JayQcDmMMent7sY5qamowpkTPqBIWFhYwBpsN3h92ux395nEmqhDBtbJ6Sf0uz70U12W1j3K5jL+lSkESMEkkpdGSn5nJZLD28vk86IXdbgdDxpCGVqlblhVkzGazpp/md/Bns9kMemE2mzUDMT+vVCqhL/y73W5fU0fscrkw/+VyGW15NlVRDPWCAQMGDJxBbMjpMrVXSsFQEAwGcaJIlwzpWWAymSCWM0exbds2cG3t7e14RjKZBBcdCAQgyj755JM4qZnjCAQC4J4OHToEdYXL5QJ3a7FYwMl5PB5wSqvdUIgq6gXmQuvq6tCX6upqWLz5u87OzjVFDrPZjL6WSiVwwMFgEBwiG65kX1OpFMbUYrGAU6+qqgJ3FAqFwGFtJlh8jMVi+Gw2mzHe4XAYhsdIJILE0rlcDm1mziObzcKLZGRkBOJXOp3GuDQ2NoL7s1qteDZLQTt27MDYHjlyBHPV3NwMztpqtUIqGRoaQgJxfoZ00bPb7eCcpHvZ0NAQ5pNVGHNzc+AEs9ks5sHn82EtFotFtL+vr29NTku6TXE7lVI0NjaGa7dt20ZEFZXUeqkvXyh4fqQ6QIrnq12u+LMSOaqlUZD3Tz6fx/52u93or9VqxZrO5/MYG+mSxRJzKpWC5Gg2mzEXZrNZ4775M3PIDocDqplUKgXOUxr3HA4H+ssqzWg0qnlw8Jpwu92ayyG/L51Oo02876RKsLq6WuNoec0qpfDuYrGoubithQ29F7gxnZ2dIJg2mw0EzuFwaK437ALU3t6uuSIRVYgy6/KUUnhGuVxG3spt27ZhQOvr60H4uONOpxN6XqUUCLrNZsMiNplMIMypVAoqDda55fN5TRySVnC+tlwugzBIXR63bWRkBPe1tLRgUU1MTGDAd+/eTYcPHyaiZ1zvYrEYxMqFhQUt+TNPoN/vhy4rn8/Dmr6ZVm5eUNlsFjrjQqGATSVdsoLBoKbuYFUKrw273Y7NYbfbQQSVUiDoSinN3Uv6PRJVNgH3uampCRtpcnIS7ZOueYVCAYcetzMQCGj+3Dw/zc3NICItLS2n5OVd7UrEenuXy4Xv0+k0npFIJLS54rHgvnq9XqyT6elpeMccOXIE30svndNt0OcKJpJutxvzvLS0hPFwOBxoq91uxzxKPaf0DWe7g9R353I5zQ7Dn6UXCs+h2WwGEZSePnI9m0wm0Auz2Yy1Ipk7JtaFQkEj+JJwr4ZURch3SN3zejpuvk/2L5fLYa17vV70JZfLaQc3f16tJmEY6gUDBgwYOIPYUL3AFtbq6mrNkMDcQGdnJ05qi8UCsXhpaQmcrPSpk0YKFj1DoRDuSyaTYPsDgYDGaRBVTjbmbqX/XyKRgOiWTCahMvB6vWg3195qamrSIsj4faFQCKdbNBrVPCCIKlyXtEqyaiOdToNTl2qHVCqlWXWJKhwOc+0ulwvceVtbG07h8fFx9DEUCj3ncuXPBmy5V0rBY6GqqgrzPT09DS40EolgDHt6erRoH6IK189zPT4+jjlramqCekUal2prazGO3E+n0wluKJPJYL6lxb9UKuHZ0nIsLe78uxzn2dlZfO90OtF+Fvt7e3vhhSD9s51OJ65pbGyE2mh8fBzcPkt/NTU1eG48HsfnlpYW7BW73Q410+HDh9dN8v5CISO01gpEkJy4NCBKSO8FHmer1apJKFJsl37JPO4yApE5Qrvdfor/O4P/zufzpxj6isUi9rrT6dRUYoxSqYQ1K33MWUozmUya3+9afvbSaMZcqs/nwzMk5+p0OjWjGz9bGgvXw4bqBdabSZcmq9UKsbmhoQEinc1mg2iztLSEjrB4srCwgN89Hg9EeekiFI1GoT+MRqNY3PyseDyuOVczvF4vxL+FhQXq7+8nooo4zxubCc3Kyor2PO5XNBpFf61WK4gxEwi73Q7CLfVlU1NTIFbBYFAjwKxDkuItD7fL5RwHQWEAACAASURBVNLUGdxX6T4Vj8cx4dIC/ULBG75UKoHA5XI5EH4ZUjs0NAS3u5MnT2quQkQVQsw6dL/fD/cxm82GNu/ZswfEva2tDffyuJTLZRDdkZERjGc6ndbEUd4oXV1d8BZggl8sFqGK6e3txfzNzs5C3bS0tIT55v8nJycx9n6/H4EgMhw1l8thrnbu3In5YUIgw2KJngkMiEQiYC6amppAnCKRCDYsj9dmgRmbQqEA4uXxeLBePR4P2p9KpdB3vofoGaJbKBSgEgwGg3h2JpPB3K2srGCtuN1uEB9eV6sjt7hNSimMnwxHLpVKGD+eexlIJIndag8gfrYMxec1U1VVhTbn83ntkOfxCAaDmHMZfcfrVXrh+Hw+MATS+6ZYLELFwmt0NQz1ggEDBgycQWyoXuDTym63Q0xKp9M4CVdWVuB3mkwmcTK1traeEhsfiUS0U5N9HYeHh3FtW1sbTo9EIgHDHBvBuAAlUeUU4dN7enoap2NLSwsdOXKEiCrcBZ+8XIK5sbFRMyQxx9PY2AjuKBgMaiGoRBVRn1UKY2NjEGFaW1uh5Pd6vRCffD4fvufT1ul0guNramrSYvhZVF1ZWYGxMJ/PQyTdTMjkItzGnp4ecA2Tk5Ow3IdCIYyhzWaD+Mj9zGazuM/r9cIAGQgEwEFMTk5qSXyY45DBDLy+urq6wMlIlZQM+RwbG8N7eL3Mz89DxTQ8PAxuPpPJIBS8qqoK3CarO2Tei6GhIc04xu3w+/2QrNLpNLjDEydOEJHut8r1tnh8maPyer2akz3vm62CxWLBPBeLRaztXC6nGbtk0hbm6HgvyTHg5xBV5kIG1fCzpZFaWvZ5zcgcCtILweVyYY9I8VwataTBTKouGDabTQtW4N/XMhQWi0XNR5wl0rVCicvlsiad8meZ3Ep6sfAcb4QN1Qu8Uebn50EkLRYLdJHBYBBil9frRYOCwSA6KB3aZQILJqiLi4sgqocPH8ZGnJubg1jLA55IJCCO9ff3g1gnk0kQgdnZWQyo2+3GvTywsVgMhGxkZAQLRrotJZNJLAKZSIeJS1VVFQ6TpaUlTa/Nzz558iREX+l2wsS6vb0dln4WTYgqk8ZEIhgMnhIPvxlgF69isQgRKJ/Po8+lUgn9K5VKmk6aNySPldfr1dwDmSDFYjFc093djTmRGdVYHeD3+zFPy8vLEOGqq6sxP3Nzc3heT08PkvDweO7atQvXlkolzf2Jn2cymbRr+H6+tqenB/M9Ozur6Wn5+oGBARzqPBahUAjj2NLSAsI9OzuLeTObzVC1zc/P43lMADcLTORloplEIoE1KlUhMgiG/yZ6JsGUzMEh80isjmRkwl0qlfAM/t9isWhZy3gfzs3NaVGr0j1VRs/xdzK6UXobMKTHDR/EVVVVWsCEfK5UM/HnRCJxSiIi6WJntVq1qDfui8zr4HQ60V8+2FfDUC8YMGDAwBnEhuoF5kZbW1tBtS0WCzhg6buaz+ch7o+OjuK0Z6NIOBzGtVIkl4pxKeJ3dnbixOLnSjHOZDJppyYb9Hbs2AGDV11dHThjmRWLrdI+nw+cgVIKJ9rKygq4WubCua3cV1ZR+Hw+tJmItFBo5n7YT1f6mk5NTYErHBsbg7EnnU5DtA+Hw1vivcDjIw0f0l81FotB4lhYWNDizVen3ZudnQU3nsvlIPavrKxA3Jc5mGXOUp7XyclJrI2uri58PnnyJAygoVAIagmr1QpxX+bcYBXFzp07MQ+1tbXwTrDZbFBPsTQh01GGw2HMay6Xw7ulN04kEkG7mUNaWVlBe2KxmGaM42tkCkGz2awZgjcTci3ymEvO3+PxaFwlt8nhcGDPcpstFsspobZEup+xDP12Op1ajoPV90m/YKkycDqd4JZTqZTWJm4zo1AoaOMoVVEyby//LrMEyoxkTDt4bRNVpEkpta4eO7fbraUTWMtfOBaLnVZ6MThdAwYMGDiD2JDTZcNRNpvFqT83NwcdXmtrK7jRmpoaLWqNTyfmGGOxGLjKWCymRZ6t1s8RVU43fg9zLR0dHcgXGg6HcaI4HA5wh5OTk+DSZNIZbpt0E5NK7+XlZehWm5qa4AbFvqZerxftn5qawjNSqRQ42fn5eS1XLJ+Q3K9UKoVxTCQS6F93dzd0RVNTUxi73t7edX0pXwiYe5+amsIYSM48l8uBY4rFYpofNfeF25hOp6HDqqurA+fZ3d2tJX5h6aKurg5rgrmDhoYGjEU+n8dcSR/JqakpjK30l+Q5yWazWm5knp+ZmRnNJ5alt9XuUfyZjb1utxvv2bVrF9oxPT2N/nKbq6urtaxVbIswmUxYo6ufvVWQyb5lohYZ+suQtgan0wnOkr+XxieLxaIZtqRRTUZtrna5ku+UhqrVfq7MQUrulblQl8uluaDxu+12uxZ+LZPbrIbUzcqEN6VSSat2wc+Q/r28j+VelC5vMpJtdUWMtbChIY0XealUggjZ2tqqOY3LEirSAikd34kqRItFOu4gUSX0l0W66elpKMNbW1shpjFBnZ6e1hTqMtaeReb+/n7ND1HGfRNVNj1vFhnAUCqVQFykMpw3ivTts9ls8G6oqqrSDGzcbynC8CSEQiEYj6qrq7XcA/y5XC7DaCkNLlJsfKHg8ZTGuWKxiLkKBAI4CBsbG3HohUIhLT0hkZ5GcXZ2FmMkRcJoNAq1g/TD5TEOhUIYZ7nW8vk8iGJtbS0MPNFoVFsHRJUwWz7QFhcXET5stVq1dbQ661wmk4Fh79ixY1pmNF6XNTU1eIbNZtOCDni8uL/j4+Mg1uVyGT7jiUQCa9vr9YL4b7YhjQm+zJLm8XgwTpJASEIkAyFkSkVpuZeGNJmJjMfP7XbjeklQpf+3FPGlX61UG65+htlshnowHo9rCe95fZhMJhykPD8yWMlqtWqJ0rlNuVxOyxmzumKMVB2USiVcm0ql0G9J7zweDw7V9fJrGOoFAwYMGDiD2JDTveCCC4iocjqy4UueDNlsFtwwkR6hw0YiFjdkMcrFxUWIkM3NzVqECWNxcRGqBM6varFYYIBIJpMwrMgMYVVVVeDEu7u7ITLLpMp8wjY2NqLN4XAYJ2UoFDql3lIul9OKWMoinKyGWV5eBlfY0tICrpBPTR5DoooxgtUZsoxJS0sLxkb6drJP6GaA39vc3Kyd0Ny+2dlZXCPF4ra2tlMSzUxMTIBr3LFjh2ZQkG53bMyqr68/JZR4YmICKo+xsTGMbSwW0zhm5tDz+TxUFywVyBDOdDqtjSGvO5nMm7lAv9+vzTFLFB6PR6tVx9yLx+PBuuO1UyqVsDZisRjme3R0FOMl3+P1enENR3duFphTz2QyWo1Bngsp3cnw4GKxiP5yv2SxAenzKt3HuP9ElXGSUXz8m+RGpWFLctyS010d4i19gVf7hcv1sdq/V/ZFJqWX+avT6bTWRx4PlnIsFovGtfMzpCoil8vhGYFA4LSc7oY6XRZ5t2/frqWMY9jtdhAWuZCk2MKLX4b1Li8v43eHw6HVWJI6QSaeTGhdLheImvQ2aGhoQEayhYUFTW/FC4XFrrGxMQz43NwcJq2jowOifz6f1yzXRBURkwljW1sbFnR7ezvaIUWOxcXFNRMiM5GTlmO/3w9Ck81m4c+5tLQED4DNBC/qgwcPagnimdA2NDRg0+TzeYyn3W6HWMz3BYNBrbKHzEfA38/Pz2MOZZi2HFtWD5VKJcxJS0sLDoLa2loQ/3Q6fUqBzHK5DJ3u9PQ0nrGwsKDVNJP13HgseJ5cLheIZ6lUwkERCARATNLpNPrF30UiETyvVCphbAYGBnA41NXVYX2Vy2U8e7PBcyvTOUqVQiKRwHxKNVqhUMBBxP2SAQepVAr3DQwMaFWHZRECpg9MhPL5vJbdi5FOp7W8DhsVt5S+uTJRusfj0QI51srZwO/0eDz4PZFIQNVZLBbxfaFQ0DIQ8v+yyKbUM7MaU1Y4kR4T68FQLxgwYMDAGcSGnC5zX+FwWEvIwlxGMpnUEk4wdzE/Pw/DA3MOzc3N4AhDoZCmhJYnHn8vazyx/2U+n4exa25uTqvYyVyOLMvM3DnRM2JGTU0NjD1WqxXvXlpaQjSZTAQjs4ax0ejIkSNox/z8PNQLTU1NMNIlk0mceNJgwCevNFxNTExoJail6CZVEpsFWZtMlirhk7uurk5LZM1jFA6HIeZJ30vus1IK0W6PP/74prf7TIOlHVlSKpPJQKUmjcQswUhviKmpKSRRSiQS4CAbGxu1tbmZkNFTPLeSw5SJk1b7sfJe5r643W7se6fTCfXOhz/8YS2MWUaOreYUV69nhgyvXc2hrv5+vcxdq5Ofr36P5Dilv/Hx48fp85//PBHpifzls9bKHex2u/HMbDarqWN4TUg/4vWwIdGV9YxkhnmG1+uFuN/c3ExPPvkkEVVczXiBsX5jdHQUFv+mpiY85/Dhw9DLJZNJWLkPHjwInbK0fLPoJpOiLy4uagPMhDuRSOAzi3nt7e0Qo2Q4o9PpRH6Gbdu2wXrP7kZzc3Mgytu3b0e/FhcX8bzR0VG0OZ1OQ1UiRVrehFKv3d7ejkMqlUpBv5lMJrEBNhM8Jh6PB+Lg008/DSKTz+fRv2w2C1ExFArBK0CmwGPi09XVhXE7H8BqieXlZRzC27ZtwyHDv+/YsQMbdmlpCZs4kUho1n9pC9mKoBci0hgY6d3DkCHX0g1MiudMcKTlXlaA6OzsxDo+F1EsFrEHwuEw9q/00JDzI3XLPF4y5FkppYWXny61o6FeMGDAgIEziA05XT6x0+k0RPLx8XGt3AdzRDLoYGlpCWoFNpCEQiGcuNFoFCoDWU1W1ltqaGjAZ/59db5a5rCUUjDUnDhxAtxwbW0txDg+9aVKQZY0OXHiBMQnWZaDxcq+vj7NiCfLy3CocUdHB4xD0orJHE5dXR1OxHg8Dg5flpZ3Op1aNiRZTXazwO1tamrCyR0MBjGeMoOSw+FA8Mfo6CjGUwYfSEnkdOWnzyWwuqWrqwsGxIWFBS14gP9naUaKq6FQCAaz1tZWrOdgMAiV1GaDuTan06nlymXU1tZqRqK1SqnLUja8Fk8nMp9LsFqt8GiRiXJkaK+sLr5WIvTVeX9Z+pEeIeu+f6MfeQNJ63JjYyMsxjt37oRFPxAIgNB6vV6t2gNRZbGyO0s2mwURlxnfZb0xabHk+4aHh0E8a2trtQxIMhM8615lMAaLxZlMBotKxnzLtG2ydDMTvXK5jL7a7Xb0r7a2FvocGav+1FNPYSMyAQuHw8hbUSwW8TxZJ6urqwsT63Q6t0T3xwfG+Pg49PMrKys4rGTVAaUUFmWxWNQSshNVDgxJfKT+8FzHnj17iKii6uKNJNeoDLSQa5gP58bGRhxwMotboVDANZsNqceVARFMXJeWlrToL/5+LaIqXbmsVqumFz2XUSwWtZSWa9WBk5FzjEKhoI0vj53JZMJ+kPRlPRjqBQMGDBg4g9jw6GKOa3JyEgaliYkJzWFflvFhDliekMz9Op1OiN47d+4Et2UymbSwVDbElMtlnEBsgFNKQWQqFAo4eb1eL8T2Xbt24QTq6enBCc5tXlhY0CoUy7hqWQadxQzm7GZnZ9FXn8+nBUpwm9va2uAQLcvM8+k5ODio1YaT1lIWd2TZbr/fr+UM3Swwt51MJuGcv2PHDhjzstks1C7V1dWaBMDzymqejo4O9GnHjh3nlRjK8xoKheBJEw6HIZmwZDA2NqZZtXnsisUi5nV8fBzcUDqdhpSz2ZBJu2WYtwwMkNKIDABh7yPJ3XJfpUS6mZWpzwbK5bKW1J3pSD6fX5NLlRIK0wWHw6GpHWS+iNNJBBv+ymJlKBTCBFZVVeF7pRQmQiZ97ujoOMUKODMzA/2oz+eDjqyqqgr3LS0twYI+OTkJAibTK/J3dXV10AtLa7AsICnFJ9azSVct6d4jCbcMVmDxsFQq4XeZb8Hn86Gu2+TkpObYzbpoXrh79+5FOxcXFzWizOJOe3s7Nmc8Ht8SHSlvmkAgAKLAbSLSY9Blhv+qqiqoUnguo9EoxiWVSm1qsvWzDR6nXC6HzVZdXY254vltamrCelheXtbc6niNLS8vg/BNTU1hjjcbTCBkfTmXy4X5rK2txborlUroi9vtBvPDhDuZTOIgLhaLUAluBSNwJlEqlcBMyUAtmTSH50r2VSb3sdvtWlUTZjxqampw6K4HQ71gwIABA2cQzyqJeXt7u5ZhiE/pVCoFaj82NqY5CDNnwFye2+2GWO9wOKBeWF3biEMlOzo6YLDY7Pj05wLunzy9kskkOL2BgQFw/plMBvkiFhYW4J3A/riLi4vgJn0+H7jMWCx2ithOVFFXbGZ2MQZzLzIIorq6GiK02+0Gh59KpWDIHB8f18K+iZ4ZH37u6U75cwlsRB0bGwMnOz4+jrUtE6LLueR9MDk5ifFtbm7GOpDBQZsNKWmtzrBHVFnHshQ5X+NyucDhyhJR3C9pcDrXDWpms1lLWSDr/rF0I9OH8rjI/B6JRAI0TCarz+fzp5T8OeX9m9gXAwYMGDBwGmx4ZLE7kczKI/38stksTvLu7m4tny5XveXTsVAoIKLLZDLBMCczGcma8dls9kWhsOdwR5mvNZfLoV/lchknW0NDAz311FNEVOFkmctnzqG+vh4n7MrKiqbL5ufJAouBQABc9GaCx1tWxy2VSuDCI5EI5komkO7r64P0w5ya7L/UTZ8PYI6WjaZEFemD+87cr9/vx5zt3LkT98XjcXwvo7ssFgskhs2GrFDLHGmpVNJ8c6W+UuaNlZm3+D5ZpJK5udVhu+cazGazlsSJOX/J7a8V2ixz9kpbh8PhgIQnEweth2dVgl3WQpMseHNzM8Jrh4aGYPAaHR1F41il4PP5NGdivi8QCIAoyUzwVqtV85E7W5Chuixym81meFEsLS1pQR+ce2ByclJLwE1UEdu5kuyuXbtgwe7q6oKYNzQ0pNVv2ooFLktiM+GQPoqDg4MgCoVCQUvDyf7OPK/xeBzeEMPDwzAqng9gBqCmpgaHic/ng3eJzJTH8zczM6MZX/hAikajIN6zs7NbJqLLEHg27vj9fuyrfD6vtZvnXKrMZBl6Zqry+TzW/LluSCMizdtAGv2ZceS+yvwU0pguy83n83kwLNIIv+67N7kvBgwYMGBgAzwrTreurg6n4MjIiOazy0a1ZDIJlUJ1dTW4VA7PlS5gfr8fqgun04ln9/b2grM7fPjwi0K9wEYnh8OB9siEz+l0Gjlvu7q6EAXX3d0NDpG5xkQiAU7RYrHAUBOPx7XaTFIk5es3EywmFgoFrbQMz4lMqu7xeNBOWeeO7+vo6MC8NjU1Qf1wPoDX5fz8PPptt9vhNsQJlwKBAOZM1vuSieztdjv8fru7u7csMZDkYlmikS5jMiFLuVzWfE35s0xivjqZP9G576drMpm0nMIyQo+xOkMgkZ5ZrFwua+6cUmJ8QX66sn4SP7S+vh5ii/THHR8fx8KUk8yNT6VS0I8ePXoUnyORCAiLyWRCIcgdO3a8KKykrNNaWVmBN8LExAQmIxAIgLjOzMyAAI+Ojmp5JIj0pMomkwkbr6GhAR4adrsdoqrD4dBCmTcLrHOy2WxaWXP2e3a5XMgNcOzYMeiyY7EY2sw636GhIS0F6FbkijhbkDXxZOJ79vLgcbRYLFgn4XAY62R8fByieCgUwjh6vV4EVmw2ZAg5Hw5Wq1Ura87tLhaL2jVSXCaqHMoyw9ZaycjPRUidrs1m0/ooUzrydzKjHn9vMpmgPpTEWiZCX/f9m9gXAwYMGDBwGmzISjKHNjw8jJM+EAiAA41Go7C619XV4fROJpPIJcuGpe7ubnBtfX19mmjGhrShoSHau3cvEVV8H7fCR/W5Qlqw+RRkjoVIr7tlsVggam/fvv2U0jYnTpyAuuXkyZMwmJ08eRIGkJaWFk18Y7/lzQS/d2RkBNyLw+GAP+6xY8dwutfU1OB0j8fjaBtzCoFAAJx5IBBAlNb5AO732NiYlpWK50RmmmNpp6urC5xkY2MjRNChoSGsJVmdd7PBe0lycDabDfOWzWa1dsgcutxuuf5kWfatygF8piG9sfL5PPorI2zZa8jlckEdV19fD+k1lUph78gQ6ZqaGly/HjacefYwkM7cxWIRxHB2dlZbbKzni0QiINjceIfDAWdyu92Ojb+8vAzCXVtbqy2arXIgfy5gVxCTyaQV2eSFOzk5iUlraWmBVX9mZgY6Uh7Hbdu2Qe/tcDjQP6kzlFZkl8sFUXUr+mS327XKFByYUS6XMa9SDO7o6MBCYxWTVK/EYrFzXt8nwYR2ampKC/Plg5O9Ng4dOgSVw+LiIoIjpqamsEm7u7sh4udyOeQp2Www4ZRZzaTeUuozbTYbDo50Oo3PUrcry6fzfee6yxiRrkKR/dlo/VqtVhBUs9ms6cb5e7fbfdrQ/fPj6DJgwICBcwQbcrrMEZVKJYiNhUIBp/f27dtxOs7NzYFbC4VCELOZI5SZu2QZa5ksZHZ2Fieyz+c7bTjdmYB0FOd2Sp/LwcFBGJc8Hg84mD179qD8D3t7yPLXFosFv/f09ICjkCGnTqcTapjNBJ/Qc3NzWmlx5uwWFxchiYTDYRhnGhoawLWzIe3pp5/GvDY2Nm5Je88WmIPv7+/XqlTLUu9Eug+3TFweCoU0IyMbjKWUs9nguZViszT6eDwezFexWIRkls1mT7HYOxwOrcotG93OdUOaUgrqBcnBy3gEHiOpUonH4xiD2tpaGJ6JngkZN5vN2L/rYUOiy6KlTFeXTCa1VGgsngYCAWzOhYUFiF5MlBOJBBapLDpJ9IzeUimFzS6rOpxNcJs9Hg/UIDKt29TUFNQIhw4dQnBAoVAAIeUNKROvZzIZBJMMDQ2B+KXTaRBp6Uq2mWAxt6+vD3NmMpm0ZOW8YYPBINRGsVgMi4711Z2dnSC0iUTivPJeYFE9m81i7kdGRpCNi9dqKpUCEZV5NOLxOAjt7Owsxtfv9+N5mw15YMsy6TzPy8vLaLfU9Uq1gyxWuTrLlvz9XIU8hIiecX/MZDJa6kYifezMZjPoQSaT0VzNmImUbnbrwVAvGDBgwMAZxIacLp9o4XAY4qTX64W1WikFap/JZLQaYsy+8wmQy+Wov7+fiCrcAItrvb29WkgeG+ncbveLItyQwwHD4TC4u66uLrTTbrfToUOHiKhiSGOuXebe5GdMTU3Bm6NcLtOJEyeISLdyV1VVgRsOh8Oap8Rmgd8l1R1WqxXv9Xq94LxPnjyJ+S6VSuCemLstl8v4zmazgas/H8Acrazq29jYiMARWVOO17nT6dRK+7C6qba2FtzV4cOHNU5rK2Cz2TBvMrduMpnUDGlrBUfw3pWJ0M1m84tiP24GZJYxmag9l8vhe967xWJRy5nB16bTaajjrFar5vt8OmPysyK6VqsV1RssFotWB17qkNhqGw6HIUqx6D07O4sNmUgksOgKhQL0ozLNWjgcRgDF2QQvXOkKJMuOSwtvNpvFRs1kMpqlk39nPW4wGMS1sjjeoUOHEJhgs9m2rJYW94P1d5FIBIci5xYgqhwqqwktEcGrQlbwKJVKp3WXOZfA4yBdvObn508pOFpVVQWR3e/3Q1w9duwYCFw6ncYBV19fvyW177gtDPYEymQyIJg2m02z1jNxkeB9n8/ntcKuL4ZcKJsBGRzh8/m0AgfSw4SoQu9Y5WCxWHAgyRqOJpNJqxZilGA3YMCAgRcRNuR02WOhvb0donUqlQL36nA4NA8DNprV19drBhqiiojNvp0y7eHExATE70wmo5WSOV0p4zMBmWCdLb2xWAxqgqWlJU1UYe5HSgE8Ri6XC2HCsVgM1s+uri6cpjJhvNls3hL1AnPv4XAYBiBpjZUlmTo7OzFXyWTylPpgc3NzGqf/Ygjd3iwwRxsMBtGv+vp6qJBYCnG73TAoy5DgQCCglUDiPbR7927sha1qczab1YIdeB/KAABuL38v0z/yd2tVAz7XvReInqFLsi/FYhGcrCxPxutb+j7bbLY1g0Wezfo3OF0DBgwYOIPYkCxzBNn09DS422g0ipPSZrPhxK6trdWML6zzY5/FYrEIvUcsFkNUj8vlwulx8uRJcHZKKXBVZxN88i0sLMAdyuv1Ii9uVVUVTsXa2lqcfiMjIxrXTkSam9zc3BxcwxYXF8E5lkol6H2bmpqQ5Wwzwbq+Uqmk5YGVuVV57k+ePIn2u91u6Lw4e1Z/fz/Cu2tra/G88wHs1iX1dDMzMzCi8u/xeBzr2ePxQDqKx+NYP8ViEfO9sLBAu3bt2pI2s3RYKBS0kli8x/L5PL7P5XLQ6cqkVswFStcq+YzzATKRO9OzbDarGcqIKntd9lsmiZff89hJiWA9bPgrb06PxwPRqFAogHgkk0mkdrRarZrvJhNgDpiYmJhAR6QhwWKxYKGEQiGINouLi1A7nE2w2N/U1ATiGovFYOyKRqPwxUyn09pBwRuLx3F+fh5iu0wGb7fbtZLQMpsZGyc3Ezw3VqtV8yHmRTQ3N4fD1G63g8D6fD4cnPydNCjI8OHzAdJgxioFm82GuZdGZD6YyuUyxFEZKJHL5XAgWSyWLctRwZ4V0rNIJtz2er0gCpJAZDIZrF2eYxnOKo1I53qot1SlSELrdDoRzMJzmMlktNwqkljz+pDjlEgktND6tWCoFwwYMGDgDGJDTpcpfDQahdg/OzuLUzqXy2ncAEdjRaNRXMNqiZWVFXDIs7OzODGcTifEsenpaYjZFotlS92lni34dM9kMpp/Jieu2bZtG7jhxcVFuIFZLJZTwphbW1vxjMbGRpyIXq+Xjh07RkSVDGZs0Noqv1c2CLpcLvSPiOA73d/fj/a43W70o7q6WqvoTFSZSxatqqqq0L/zATJZE3NAdXV1p+SK9nq9WMPz8/O4dmpqCmoaomfG12w2w+99syGTqTO3ZjKZcqzCuQAAIABJREFUNOOsrA8mc+jyOpZ+vNyv1XXWziesZVTjz6sjzGRlYHm9dLM7XfqCDYkub8ju7m6t3LDMIMaic3d3NzafUgqLikXy1tZWFG0MhUJaTgNepBaLBZ0ym81bFir5XCA9D1isKhQKWvJnbnO5XMZGtVgsuF4GIPB909PTmv8uq2lWVlagH5yamsJG2EzIQqA8Z16vFwRibGwMPtKpVAr+uZFIBKoPJjzyMI3H4yhBfz5AFixkdUo0GoXKTOrhWbXmdrvBiDQ0NOBw7unpwTooFouwCWw25Brl9eXxePDuXC6n7TFJaFn9wfcVCgUQcVk55VyHPIRcLpeWi4L3g1S1MIORTqex7iWzmMvlQCcKhcJpva4M9YIBAwYMnEE8q4i0EydOgJupqqrCqb5t2zYYxI4fPw4OIBAIoOwO31cqlZAkO5/P4/TI5XIwUuRyOUS+1dXV4XlnE/KUY1RXV0MF4PV6Nf9kPuUikYiWa5io0j+ZaY0lCZ/PBy+Fjo4OhI7mcrktSRzNbairq4MKx263ow1VVVXgcDwej5Y3dHUWMb/fj2iseDyOkOjzATxOs7Oz8DzI5/MYA/baaGtrA5c4MjICtZHb7cbYlEol7IWpqSk8b7MhIwL5c7FYhMRZKpW0THe8x3O5nBaCT3RqGZq1kpyfi5CGtGKxCI41k8mc4sGRSqW08lbSCMnjVSwWNWPaCwoD5hd0d3cjJLK3t1dLxciLbfv27RCtx8bG4HnAomyhUIC6QKa5k+LawsICPiultNRpZwuS+LBqQAYtHDx4EG41oVAIY+Dz+bR+8TOYeEciERxCVqsVXgozMzNaUvTTWUKfD2ToLx8YMzMzCJQ4ceIErPFKKYjW0mIrNyaPkczDcD6Ax6axsVHz5mB1GKuB8vk8Nm5NTY1WL42J68zMDMZmz549W6YX5TWqlML+zeVyWj4FSTgY+Xxe0wdzX6Xq7HzyXpAFN/lzNpvFPEqvDkZNTY2Wq0KOKdMAh8Nx2rk11AsGDBgwcAaxIafL3Kis9Hv48GEYWZaWlrQ8okzhOzo6tGQ5RBWOUfoQypBgtrK2trbCwivz855NsKEwGAyCw5HGpZ6eHs3YxX6ZpVKJdu7cSUTPqCisViue19/fj2tramrgFZDP5yERyLJHmwk+oWWNrEAggFO9paUF3HkmkwFHPjMzc0qu0MnJSXB2FovlRTFnmwVWDczNzYGTSSQSmB9WizkcDkh/wWAQ3GZNTQ28eKxWq7a2eb63qs0rKyvYYw6HA9yrLMFOpAdTsDpBBkfIMOC11A7nIlZ7JMiE5VKVRlRZ09IQLseIr5UGOH7ORtiQ6HJZ8KqqKmxUGSUlF1V1dTUmZWpqCq5T0iWF9b+Dg4O4b3l5GZtWEu7m5mbod88mWB0wPj4OohQKhbBYZWWFxsZGEOZkMolFz+J5XV0dJurYsWOa8zrD7XZj7GKxGDbRZoIJSCaT0URkbrvVatXUQnxQyM3Kh2ldXR0OoO3bt2PNnA9gwiirAdTU1MCmwQdiJBKBaF4qlbT72MNhamoKn71e72kTXT9fSL2rdP1iSK8aWQUmm82iTbzOi8UinrG6isS5jNXtXyvXBPdVVuCQRFeOjdlsxn6Q+RvWff8m9cOAAQMGDDwLbMjpsnEnlUrBMDY7O6vV0mJRpKqqCo78xWIRJyhf6/f7wVXJCpxmsxnWep/PhyxcIyMjUG9w6Z9AIIDE3+3t7TiZFxcXccIHAgFwltu2bUNNL9lO5jBlcuodO3ag/WazGVZ9NhRmMhlwnQsLC1qCaOZ0lVKaNwCrZJgLSqVS6F88HgfHKcVNp9OJtm4Vp88ndDwe1+q48Yl//PhxcNv5fB6qhpaWFvSF2+52uzHHMj/v+QA2YoZCIS27FHMy7JtptVoxBqlUSuOkmGv0+Xy4ZmVl5YyUYGc1SKFQQJtliZ5cLod9Kkvz8LUyeEKqGs51yITtRKQZCHmeZb5vmTdXej2w2lBWW06lUqcNjjg/RtGAAQMGzhFseNwy99jY2Kj5JMos6cy9Hjt2DAaXxsZG6ASZS3K5XNCLDQ0N4dQcHBzECVsul7UoLq6yy7rGQqGgFdVj3XJTUxOMdGazGRymNPSxi1c6nYautaamRnP7YJ303Nwc9G/cD7vdjra1tbWBm25pacG7ZXSL9HXl9iQSCTxPKYX3Pf3008jPe+TIESTKqa6u3hKOiN+bz+e15Drc3o6ODhg3pT7f4/FoFUSISDMIBYPB8yoijf2rE4mEVo6FpRzmJMvlMub4+PHj2AcWiwVj6vV6wVVKN8nNBnPWJpMJUkcmkwGH5vF4sA+LxSI4OqvVivXNc2+327H+stks3CG3Sh99piAre5fLZS0qTxbqJKrMG69/GeIuw6xlMiCTybRmNQ6JDXc0L654PI6BjsfjYJ+Xl5ehdvD5fFiYkUgE4qm0+LPf7epyJSzWSz/dYDCIzywOx2IxvK9YLGLjR6NRDIjNZoN3RS6Xw8ZgA480inR2duLdx48fB7ErFosg1mwsWVxcxH0nT55E2zweD8TQkZERLTiCCT2/IxAIQPzzer0wyLS1tWFTuFwuLdiCr9lM8PNbW1vRtmAwqImSfDgEAgEsoqmpKcwni1bFYlFLkM0W/fMBzGj09vaCYJbLZRAiZiiUUlDT+Hw+bePyATc6Ogrmorm5ecvK9ch8AEwI8vm8tr5krgFZdkqq/Lj9jHw+j/19PuRekESXD55SqaSpCvl/6dEgx0gmgJeH3elC9w31ggEDBgycQWzI6TIlX1paghpBKQVuZ25uTjuxmcvL5XLgoKT4IpNn8LMnJiZgfDKbzVBdSMU9c8jbtm3DtX6/HydvU1OTlnSY1RKtra3g0PlZMrTWYrFAhCR6phBha2vrKQY4h8MBsTEUCsHnUoonnZ2dWpE7Fsc4+fnS0hIMabJsSiAQoKNHj+I+5ha3bdu2JUl/+Jk8TkQVFQhz1ePj40hYVCgU8H1VVdUpJYgKhQLGuKamBv07H8BrdGhoCCG8Pp8P0gqrZpxOJ6SaQ4cO0cTEBK5lialcLuN50rd5s8HPzefzWmIj5k4TiQT2qaxcbDabsXZZapRZBJVSp3CB5yrK5TI401KphP3rcDiw32VCf17foVAIkoHZbAYdSaVSoD+rQ4LXwrNK7VgsFiFK1dbWYtE5HA7oPk+ePAn9XlNTE4inrH4rk5LzghgcHMRnj8cDAtvY2AgCy3rcfD4P4j89PY2N73A4tNBG9rqIRCJYQH19fegLY2pqCs8Lh8NYgOPj4wjf5D7Nzs5qWeXZsyIej0PVIJ8djUaxUZlohUIhTEg0GsX7UqkUJtZiscBbY3R0dEs2J/ejtbUVuumhoSGoEdrb26Evj0QimoqIx4vHdXl5GWqZkZERZEs7HyBDnmXiflY5MWORzWah0uru7sbYrKysYN/s2LED4z45ObllGfSkDy7vD+mxI/1xc7mcVgpeHqRElbXIe2+r8oCcDcgDhBlBolN9con0IAiZh0FW1ZA+0fz3Rjg/RtGAAQMGzhFsyOkyl2ez2bRMUsyCNzU1IatUd3c3ODqpTOYTYGVlBaLnysoKvk8mk7h2eXkZnNTCwgLewxyHTKbS2dkJkbxcLoNLk9x0KpUCp8jqgB07diBBd319vVZWiDkAeVIxNzM4OIjxkOGzyWRSy+zEXILL5dLEciI9Wqm2tlbjjJmL2LVrF8TTzs5OnKybCeZuvV4vTnrJAR05cgTissfjgcRRU1MDtQuf8oODg5BOTCbTaUWrcwk8BidOnNDmgTlWaYxhj5jW1lYtURRzusvLy5ohaqvCpWUIL3O3Pp8PbSqVSpooLLNqMafLz7DZbNinMnR2fHxcE7Ol99FqI9JaVYb5PhkJxm2ViWTkPlzLuCeNXPIamWRevlP6obN0nclk8D6pAuL1Lb1A0um01maGrJf2bOrImdQGvDB3Ytu2bVAppFIp6DZzuRw22fz8PMTiYrGIjrN+0u12g/iEw2F01Gq1Quc5NTWFRvf09IBQMlGrq6tD8ueuri5MPOtOiSoLRVa2YFcsJgypVAqi4OjoKIh1IBCATpdVJkTPWOmXl5exWC0WC56RyWSgQqmvr8cEzc/PQ+fHE9zU1ARPh4WFBahB5OK3Wq0gcrW1tVj0HBSyGWC3riNHjlB/fz++l07gPA8jIyOazlaqeoh0HXo+n9cCPljf39jYqFn8WW3F3jHSXU/WoZqamsLaiEajGPO5uTkcrnyIhUIhHAjBYBBj7vf7se4SiQTaxPcrpUAYC4WClqBf5qXggyqTyUDdIj0auP2pVArrMp1OY7yki5jP50Pq0832BOAxklnGTCaTViyTib9MuG21WkEwpTcK71O3261Vz+AxtdvtWk4VZnL4GTJQSgZgSLVGKpXC82w2G8LKZXg8j6kMxc3n81rILY+1zHAoDxVed/F4HIdkJpMBAZUeHFLFIpO6S8ZMqoiYPppMJi34aC0Y6gUDBgwYOIPYUL3AHF9NTQ2c4vfu3UtPPPEEEVU4YbbOejwe+DU6nU6ceKxSKJVKMM7IgAKLxYIwYFl3aGRkBOKdDFDg391uNzjh7u5ujQPmU7Ourg4nGotOshJrNpsFJ7uwsADj2cjIiFYunajC7fCJ3dLSgtJDLpcLp3ZDQwOeLROa87sjkQjGS1qOx8fHtSq7zCGvrKxsSZ045pqlE/fs7Cx8q4vFIt67bds2tD+VSkEaYDXQkSNH0P/29natDhhzd2NjY+A4+vr6TlG7uFwucH7V1dXgbquqqsBZSPE3FApBBcPcxsTEBLj2iYkJvNtms0G6qK6uBqfL3K3f78dazWQymB+r1Ypnp1IpLbhmdQa9fD6Pvni93jWrJ7tcLnCYo6OjW5I9TkJyrolEQqs2zeuOJQ2iyr7g/jBnJ+uiFQoFSBWxWAzX2mw2SJomkwljxvMtQ2KlcY85V76W96HJZNK4RqLK2HGbZF5f6ZUhczlLTpe5W+mrrJTS1DAM6Xu7VlCSDAGX0vzqoK7T+eluqF644IILiKjimcCNkS5B6XQaHZXEYXFxUYtdJqqIV0x00+k0XJKcTicG9OjRo9j40WgUHeEUiYuLi9gUMuN7Op3GZMv0g21tbdD7slU9k8ngfYlEQpts6Y7Gm5Pb39vbC4Lq9XpBsLxeL96xuhoAqx14Ejo6OkDwZDSfdD+ZnJzEppC1uTZTDGX1wvz8PPpfW1sL/fyuXbugMvB6vZpenOeC+9HS0gKx7ujRo5qzPY+52WzGWNTV1WGz8zscDgeIgiwX3t7ejsO+q6sL8x2NRnEQ88KXKoVsNgui6/f78bwTJ05ApSMTs3NtuCNHjmgqMhanI5EI1m4oFDolxWFVVZWWP4TXicvl0hzouU0ulwt94RpqmwVuvyQaCwsLGBuTyYQ9Ju0Kq/Wf/J1USzABk5GX0rovC3jyQSfVMVLVIPWgUo9bKBTwPF4nfr9fq13Ge1OK/sViEWuP92axWMS6isfjmDe73Q7iL6PrLBYL9gNfK20dbrdbawf3q1QqYT3Z7XbQPl4zq2GoFwwYMGDgDGJD9QJb7ltbW3GSj4yMgMK7XC5wIolEAieCrCXEp2dVVRVEfRmKW1VVhRPB4XBABJM5R6Winn+XzwiHw5qxhyFPPxaLe3p60GYZ7iudwy0WC75nDk06wE9OToJrmZ6eBkd3/Phx+O/abDbq6ekhomf8dBcXF7UcpyyWNTY2QqSSYpfMYbGZkNmkmCtIJBIQz2dnZ9HOqqoqJFvv7u6GsYq5w2PHjkFUDgaD+Ly0tARVQz6fx1gcO3ZMCyogqsw7i64yWEX6UZvNZlzT3t4OdYQslc1cnlIK10rDpDQESgMSG1kHBgbQ5s7OTqz5qqoqtNlms2FNyNBaXg/RaBRrp76+XuOyedztdju44c2GVHnwXpFVqGWy8mKxiPWVTqfByfK8SBWY5Nrtdjv2W6FQ0JL0r87LIENkZR02q9WqGV3l9TKxOlFlf6/nfyzz3vJ+4ncEAgFwrDLcd7VqQ+YgXp1nwWq1on8yQ5vJZAI3L9ULuVzutFLphkRX1nrihppMJizcZDIJEcBisYAIms1mDAw3zOfzQddSLpexEXK5nEasZSIZXkCsv6utrcXAShevpqYmHARSPyqrHrA4HA6HYeGNx+NY/OVyGYRkeXkZk8n/T05OQsddKBS0/AjSGs9j4HK5oHYYHBzE2PEit1gsILStra0Y02g0CuL39NNPb0mqRNZnBoNBiPiNjY0gColEAkTmxIkTGNvR0VEEmfD8NTY2Yl6Hh4e1BEL8/fz8PA7wgYEBEHFeU6VSSZszvs9iseBAlmqOcDgMnTtvpJWVFRBPmQBpbm4OGy+Xy8EyzgSku7sbfZEuhPPz8xBjlVJagUk+ZHn9pVIpbLRMJqOJpsxohEIhPC+TyWjW+82ELAsuI0B5HcvAgEwmoxEqydAQ6TXSZEIcWX1CVluQuQuklV/qXWWEKH+fz+fxHrvdrhFSItKiXlerQVjNIRNq8TtktJm0X8jUslK/a7PZtEOcf+fnFYtFTXcrP8u1fLo9a6gXDBgwYOAMYkNOV5ZDZy7j6aef1jKOMerr6yHSNTQ04DNzTF6vF1xePp/HaRYKhbTvWUScn58/xUq8vLysxX/zKTYzMwPuQiYjb29vB2fDxgOTyQQRsrW1Fc9rb28Hx5pKpSAmc1+lJ4FM2RcOh8GZSp/F+fl5cEfMTSeTSZyIjY2Nmk+jVDWwCF8sFrck9JLHO5fLgXMNh8Pg4FwuF/ra0NAAqaW3txdzLtUh0ojKks3OnTvRj+rqaqwHu92ONSHXF4twBw8e1LhYHkOHwwFx32KxaJZtosoaYUnk6aef1jLG8fqRqhqe63Q6jWfNzc1hXltbW7EGotEo1lR/fz88Zfjd6XRaS+7O4zUzM6Ol+pQi8laX68nlclqSdWm0kuHsMj+D5DaJ9Bwpq/M38Hz5/X70PZ1Oo4+S65TGapaEZJ6DpaUlzftjtW1f+hlLztRsNqOtkpPlsZX0orq6GlL08vLyms+oqamBVMd9XVpawhjJDHM2mw1Svs1mQx8lt78eDE7XgAEDBs4gNuR0mXqXy2W4tjidTi2ROHMz0WgUnEY8HodLGHNB6XQap4TFYoFRamhoCPdJt7JoNAougU/HQCCgFQBko1lLSwtOl7m5OVxvNpvB6XJfUqkUOKnjx49rrl/MeXk8HnADzF253W4YeTKZDK4NBoPQ3bpcLugja2pqwFFLNx6ZOIW5iJmZGS0smsfXZDLBKLOZYLevaDSKcUsmk2hvc3OzluiaOYRYLAYduczGxGPY3NwMDmFmZgZGMI/HA87SbrfDsMhzMjY2Bj27NGw1NDRgjBYWFsCFSF09o7q6WnNLY72xDHV2OBxoH3PqNTU10LmzyxxRRT/N38/MzKCP8/PzmGPmdMvlMiSVvr4+cJsLCwvg/Ds7O2GbWF0uZjMhEyfJJNtSf8pcpdPp1EJ/eb/x3KdSKfRbGpyki2O5XMZ7yuWyxmkz1qqmK6vsSv2oTBjP19bV1a2b4YzH3ePxoI+rw7SJdN/iYDCo+RFzW2VkqDR68njJMGdZVstqtWoReKfjdDeced78MiZZ5jYYHh4GsWhpacHnmZkZzamaqEJQpXjOnQsEAujI5OQkNkNVVRUmnO8rFAogGJFIBBurp6cHnzs7O9Hp+fl5EEoZmMED5HQ6sZHD4TDEyWw2i40vDx4WK51OJ0TkdDqNMbHZbBBFnE6nFipMVFGlsPrB6/ViTJuamrQQVia0hw8f3pJcBrzIpNHK4/FgQS8sLMALIxwO44B0u92YY97c6XQamyOdTqO927Ztw2Ek/TfL5TKewSqHxsZGiO9WqxW+4KOjo1hH0iMmn89jfvjdLpcLm8NqtWp9ZPXP0aNHIRIy0XY6nWhnVVUViGRNTQ3W6J49e3B4F4tFrBNJNFi9dfjwYbS5vr4e6ySXy6F9ra2tm+6fy+B5kR4LSilN3Of9JudThn7L0FqZnYyfIbP6EekG0dV5E6Q6wGq1atkAuR1Wq1VT9/Ahye33+/2aN5T0leV5lmodvl+q5mSdR7fbDTojjXKyooRUg/AYpdNprVbeWonOV5djXwuGesGAAQMGziA25HRZrJSGLxaRiCqRSywqJpNJGIYaGxtxivGps7KyAjE7n8+DM5C5RYPBIE7N2tpacBcyo5fMkMQc7cjICK6ZmprCM6TP3/9v782D7LrOetHfns7ZZ+p5kuSWWmPLGm0njssmtuwkYB55LwVkMHDDtZMKASchhHpFFSEFcYXKHxBzuTGPi28eleQRAokdHg/IhSJFyEwAx04ky7IsS7IGS+65+8xnj+v9cfb39beOWm1Z7lbc6v2rcun49D57r73W3t/6xt9HO9vCwgIHjAYHB1nbkjypsvJNdumlXMxms8nBsdHRUU5HGx0d1dLcaBclV8vU1BR/d+HCBS0YQdVsQRBoLGCXq2p5NaBzlstl1uz27Nmj9Y6juZU5jefPn2dNivJuT5w4wcf09vZyPvTU1BRryNVqlc998OBBTQulv5Om0Gq1OI1Nkk339/fznEvOZBnMldVTNOeWZXGKnHQD0FrLnlalUom1FKUUj3lhYYFdDWfPnmXNmDSnSqWipRjR93Nzc/wMS5fN3NwcW5ErDRk8I63e8zyeG0koJFOnpJZHv7Ntmy03mQolA0eyolTmtNJ60vsKtDVP+nsul9MCbDQ+27b5eZIViPTudXbcJjkixyHXQmqj9G7K/GTXdflZ8jyPj5d53NI1I8lxZM6uDIy+XMrYskKXFrBQKGglnfQwAovmR6PRYB9XvV5n+kTyz2zZsoVNyNHRUZ6gYrHIgq1cLrP74Ny5c+z/pHFs2rSJF2JgYIAfkr6+Pl7cyclJjbGI+CBoQyiXy5obhMy/hYUFvs6GDRsu8f3JAoZsNqv1DZOCidwcIyMjvMj04vX29mr+TFlGSMJvenqa77tYLPL3KwkynbZt28ZzJXkhFhYWNHYpEloyy4CEa19fH89LGIacJSILLFzX5bmYmJjgOZBsdbI0Vb78tAH19PSwfx2AlocLtDMr6LP04wZBwOuzY8cO9vvSc1SpVHgtL1y4wG6V2dlZfjZarRb7e2+++WZ+/uk+ZAv23t5ezV1G78fCwgILWvnMrzSky0MKVOkTlTSVNNYoirR4Cd0XzU2r1bos5aJstkrHS+pHacIv1cOt1WppyhQJfRpzq9XisUlXjuRhkKa/dAfQtTOZjJYvTMfIppKy5Ff6nunZdV2Xj11YWNAEM0HWHVwOqXshRYoUKa4hltV0SeOSzutMJqPl5dGuuXfvXo7o+77PmguZc67r8u/m5+dZY6rVaqxRVKtV1gpLpZLGtwq0TRLSZs6cOaOVH9LOtGvXLt55z5w5w/mapJkdPHiQNZQwDNmU7evr4x1rcnKS71dWilE0fnJykjWtsbExNnvL5TKblgMDA9yBWDJekYul0WjwTl4oFLTSRdpNe3t7X7ad89WAtLJMJsPuonq9zvfa19fHc/7SSy+xRjo/P8/rI3d50qi6urrY2pGZDLOzs1ztd+LECWYRk4QyNN8TExNa/zziQz558iQHxE6cOMHPwU033QSgHfCj9Wk2m+yWkL3fstksrwVpS5VKhZ/RHTt2sNZeKBR4DlqtFo8fWNT0ZEdoctmYpqlx9sogkqzIpOdgpXG5jAWppcr+YLQGdJ90PNB+Xunv8r5kRRqw6IKTHbql+4EsSFkuKzMd5ubmeHyO4/C80vijKNLkCGU7zc/P8/eynTxdW+ZDO46jVczSsy7Z02QJr9SaaU7z+TxbnnNzc/weZbNZraXPy2m6y7KM0UMiBcSNN97IAkcmWvf29vLkyn5KNJmFQoGF4ejoKL80GzduZHNNppHYts3CTDaOI0Fcq9W0B1dmRpBJunXrVh639PnSZPX397PZWCwWWRjIppLSXKKIeaVS4WMHBwdZEE1MTPCLXy6Xedx0Pc/z2FTftGkTj1kKqJMnT2os/4SV7CBBa9Pb28ub4+nTp9m14/s+j61QKLBZL1uHk7vp1KlTbJI/99xzPObe3l5+OSzL0jg1KDWNni+5SU9NTfELYVkWu6mKxaLWH4/OLSPdJEjDMOTPTz31FPufT548yWtBc1ypVPi7mZkZjSmOjpmdneX7bjQa7A8mQdvX16c9tySobNvmY5rNJr/0mzZt4jlY6SaPsqEozbnjOLwuJHABnSFM0jhKH6189+geoyhiQZTL5fg9jaKI11EKcFniLWk26Tqyk4zsNUbz1Ww2NZeHdGPK7AWZkkrnonuq1WpL+qEl34JshkBj9jxPE9YkT8IwZKVUFktFUaSlOS6F1L2QIkWKFNcQy7oXSJuZmprS2LFk+ZvMUpDkE6QZ0O4zPDzMu8TFixeXdDvs3LmTv2+1WhxVpJ1odnZWa4MuswZoZ5WJz8ePH+ecT6ndkSZ87tw51pYnJibY1TAwMKBF8gG9dHBoaEjrq0TaQ09PD5vru3btYs2YduPR0VHWimWk9+zZs6w5bN26ld00mzZt0qK/KwWKxEvNu7+/n7WChYUFLVAouY9pXSmCv3//fs4xHhkZYQtGKcVzfuzYMX424jjmeSSLaWBggE08SVDTbDZZSzUMg69dKBTYDSBLrclNlc/nWVPv6enhZ3B0dJTnmX4vo9CFQoHH39PTw1rZ8PAwfz8+Pq61iwHaWhQ9lydOnODxT09P83vT29vLz9Tk5CRuueWWZVbo6iE1OGkdyd5fMn9VZmtIbZIgu+XSM9FZHiwDyZ1FH5LwxjTNyxJ/S65behZkSy85ZvnuyUyBzl5sMvNA5hBLSIIdGeSj51UGxuQ4OvtAyoKIV5W9QCcaGhrih/RHP/oRX7iTmYoE3MTEBE8u+WcqlQof6zgOm7KVSoVfrEajwQ+37DwguxiQublnzx4WBtPT00v6UXbu3MmFaaGsAAAgAElEQVTjoAn3fZ8F6cDAAF9v27Zt3M/N8zw2HehF379/P4+jWq3yCzk5OcnzITMWpL9MVqHRhtVoNLS6cRK0mUyGNxvbtjVza6VA9zk6OspzIf3zCwsLPIbx8XGNoJsEJRWdSMpCy7LY93n+/HkW6DIdLZ/PszlKJly5XOZxKKV43p566imtYwOZ9bZt87oRtm3bxmt86tQpvpeBgQHeuAqFguZHB/R0NVn339XVxeObmprSmLeocwr9vbu7m98PpRRfT5qas7Oz7B6h+V8NSJY3eidkpH1gYIA3e1lZNj8/z+slSdpJYahWqyygZdcLSeYtOyZIISr9teSWq1QqvEadzFz0vWysKZuAyo1FZhnI7+meJMG6vBfpBiM5Y1mWxsAGtBUN2Q+O1nl2dlbz/8qmubIabymk7oUUKVKkuIZYVtMl7aRYLGosWLSTG4ahdbSlY2SbHNpVZaCqXq+zBlksFnlXieNYC3SQNklMWCdOnGAN5ciRI2xOyrr7UqmkmS2SoJjOKzU3WWtN2oBsFUQa3dTUFAdFDh48yLvqD3/4Q25/c+7cOXbyy/JHSRpOWkKtVtNKjWmeqtUqa8Ozs7MawfNKgbTxU6dOaQn+pJGbpslaz/bt27VEctIKSNMcGhri+QzDkNdsaGiINQHJNys/SyuIsGHDBs40GR8fZ81amsWTk5N8HVmkQWslGbEqlQrPrcznJstibGyM16erq4vdQ2EYcqnu8PCwZqaSRUeaWLFY5LFls1l+bzZt2sTvRHd3Nz+jxWKRteWVhnTjEGSRgDTJbdvWtE3ZWoggW7DT71qtltZHbalyWFkWS+vrOI4WJCVtWBYuALjEfSPdXZZlaX3P5H13ujak9uw4DrtBPM/jMUk3QalUYi2V5kWWPEt3TalU0nLBJdvdy7VhX1bo0oO9YcMGFji5XI5veuvWrdox0mdLDyaZjadPn+ZJGhsbY/+pJAbO5XKaD5ZuVvpapdpPQquvr4/9z7LZ3rFjx3gySEBLkvOhoSEWgs8++yz7dzdu3Mi+V7pGV1cXm7pHjhzhYwcGBlgoSR6JRqPBc0MCqre3l196aeLIjUJ2l5DkLysJijAfP36c06lkEYTneey6qVarnCkgMwRIGGYyGX6Renp6tHslITMyMsIP+cjICK8Frc38/LxGBC+zECgzQpJXl0olHjdlnwwNDbGv/sKFCyyM+/v7uYmoYRi8rnSuiYkJfm6npqZ4TSSpvfTNylQsEq6FQoGf28nJSZ6vkydP8kYgI+blcvmSfnorBVkcsVQWjGyDLtuSB0GgVWwRpHuLnlHZZ1DyqEj+Bkn8LV0DtAHK8XVmUXR2jpCZB9IHT7/tBH2Xz+cvaTxJ5yCBKX3Z9NwBi5kMcvOSXTcKhQK/357naQL/5YRu6l5IkSJFimuIZfN0U6RIkSLFyiLVdFOkSJHiGiIVuilSpEhxDZEK3RQpUqS4hkiFbooUKVJcQ6RCN0WKFCmuIVKhmyJFihTXENdc6D7wwAP4yle+8mM/z0qN4+Xw0EMP4eGHH17166w33H333fjBD35wza73vve9j3k/JD7/+c/jQx/60DUbR4q1j9XpA/0ax8u1SL5ayFLKFJfHtZqnsbGxFSGXiaIIf/7nf/7qB5QiBa6BpvsXf/EXOHDgAA4ePIhf/uVfBgB8+9vfxh133IFt27Zp2uanPvUp3HrrrThw4AA+/vGPL3sOid/93d/FAw88gDiO8eSTT+LQoUN43eteh3vvvZfLl++++278zu/8Dg4dOoRPf/rTAIB/+Zd/wZ133oldu3bhq1/9KoB2ffV73vMe7N+/HzfffDO+8Y1vAGiXD99555245ZZbcMstt+Df/u3fAADf/OY3cc899+CXfumXmIPhk5/8JMbHx/GWt7yFe7RdD/jDP/xDPPLIIwCA3/zN38Sb3vQmAMDXv/51vPvd7wYAPPjgg3j961+PvXv3ams4NjaGT3ziE3jjG9+Ixx9/HHfffTc+8pGP4I477sC+ffvwn//5nwDa3Azvfe97ceutt+Lmm2/G3/3d3wFol57+wi/8Ag4cOID77rtPI+O+WjQaDbzrXe/ic952222sPReLRfze7/0ebrvtNnz/+9/XNOvPfe5z2LVrFw4dOoTvfe97r3ocKdYZ1Cri6NGjateuXWp6eloppdTs7Ky6//771Tve8Q4VRZF65pln1Pbt25VSSv3zP/+z+pVf+RUVx7GKoki99a1vVd/61reWPIdSSt1///3q8ccfV7/1W7+l3v/+96s4jpXv++r2229XU1NTSimlvvSlL6n3vOc9SimlDh06pB588EEe2/3336/uvfdeFUWROnHihNq0aZNqNpvq4YcfVg888IBSSqlnn31WjY6Oqmazqer1umo2m0oppU6cOKFe97rXKaWU+sY3vqHy+bw6ffq0UkqpH/zgB2rfvn2qXq+rcrmstm/frj71qU+t5jRfM3z/+99X73jHO5RSSr3xjW9Ut956q/J9Xz300EPq0UcfVUotrk8YhurQoUPq8OHDSimltmzZov7gD/6Az3Xo0CH1vve9Tyml1Le+9S21d+9epZRSH/3oR9UXvvAFpZRS8/PzaufOnapWq6k/+qM/4rU8fPiwsixLPfHEE8uOd8uWLcv+/VOf+pR6//vfr5RS6umnn9bOCUB9+ctf1sb7xBNPqIsXL6rR0VE1NTWlPM9Td9xxh/rgBz/4MjOXIsUiVtW98K//+q94xzvewWxSRH7zsz/7szBNU+NZ/drXvoavfe1ruPnmmwG0SS6ef/55HD58eMlzAMDv//7v47bbbsNnPvMZAO12MUePHsVP/uRPAtDbhwDAfffdp43vXe96F0zTxM6dO7Ft2zYcP34c3/3ud/Hrv/7rANodZrds2YITJ05gy5Yt+NCHPoQf/ehHsCyLyVwA4A1veAMT4HznO9/Bz/3czzF5xtve9raVmMrXBF73utfhySefRLVaRTabxS233IIf/OAH+M53vsMa8GOPPYbPfOYzCMMQL730Eo4dO8ak6Z3z/4u/+IsAgLvuuguVSgULCwv42te+hr//+79nP3ir1cK5c+fw7W9/Gx/+8IcBtEnY6Zyd+OAHP8ja58WLF7mH2jvf+U587GMf04797ne/i9/4jd8AAOzbt087p2VZePvb337J+f/jP/4Dd999NzOy3XfffdqzkCLFy2FVha4S9IYSxBhEx9C/H/3oR/Grv/qr2rGPPPLIZZnYb731Vjz55JOYm5tDX18flFLYu3cvvv/97y95fGeTx87zSrajTvzxH/8xhoeHcfjwYcRxrLHlv9x5rxc4joOxsTF87nOfwx133IEDBw7gG9/4Bk6dOoUbb7wRL7zwAh5++GE88cQT6O3txQMPPKDR4V3p/P/N3/yN1gjycscvhT/90z/lz2NjY8wwthQut9ZAmxXscn7n63V9U1wbrKpP981vfjMee+wx7rhA1HtL4d5778VnP/tZpkm8cOECpqamlj3HT//0T+O3f/u38da3vhXVahXj4+OYnp5moRsEAfP6LoXHH38ccRzj1KlTOH36NMbHx3HXXXfhi1/8IoA2R+u5c+cwPj6OcrmMDRs2wDRNfOELX7hsx8+77roLf/u3f4tms4lqtYp/+Id/eAUz9trHXXfdhYcffhh33XUX7rzzTjz66KO46aabuJ1OoVBAd3c3Jicn8U//9E/LnuvLX/4ygLbG2d3dje7ubtx77734kz/5ExaIP/zhD/m6tC5Hjx5lSsdXgze+8Y147LHHALRpQKl783K47bbb8M1vfhOzs7MIggCPP/74qx5HivWFVdV09+7di4997GM4dOgQLMti18FS+Kmf+ik8++yzuP322wG0Axl/+Zd/ueQ5Pv/5z/Pv3vnOd6JareJtb3sb/vEf/xFf+cpX8OEPfxjlchlhGOIjH/kIk6B3Ynx8HIcOHcLk5CQeffRRuK6LD3zgA/i1X/s17N+/H7Zt4/Of/zyy2Sw+8IEP4O1vfzsef/xx3HPPPZdtjX7LLbfgvvvuw0033YQtW7bgzjvvvPoJfA3izjvvxCc/+UncfvvtKBQKcF2X7/HgwYO4+eabsXfvXmzbtg0/8RM/sey5ent7cccdd6BSqeCzn/0sgHZQ9CMf+QgOHDgApRTGxsbw1a9+FQ8++CDe85734MCBA7jpppvwhje84VXfywc+8AHcf//9OHDgAG6++WYcOHBAa0m+FDZs2ICHHnoIt99+OzZs2IBbbrnlZVtup0ghkVI7pvix4O6778bDDz+M17/+9T+2MVCzQtd1cerUKbz5zW/GiRMnVqUvXYoUhHWZp5siBdBOGbvnnnsQBAGUUvizP/uzVOCmWHWkmm6KFClSXEOk3AspUqRIcQ2RCt0UKVKkuIZY1qdL7b+VUpzs77oud+mcnZ3l7rv5fJ7TfAYHBzk/krq5TkxMaDmblAPpOA6fu6urS2v5TMfT9cIw5KR0OgZop3ZR99fp6WmUy2UAwA033MBjoo6k1AUU0DsOZ7NZ7uQbBAF37aWxGYbB7cpll96hoSFuGW5ZFo+/UChwVJu6hmYyGe4mWiwWufvujh07eBxynrq6urgr6XLpdq8U1A35cus6NzenrSvNne/7/JnmNZ/Pc+FKtVrluZdtqbu7u3lulVJ8f3Qu2TLdcRzO4/Y8T8sMoPxY0zT5GDoHrRf9neYzk8nwmhiGoXX7BdrrRGvieR6Pv9FoaK3KafzFYvGSdXUch59nz/O07siUAmkYBme8eJ7H46XioBTrB6mmm+Jl8dZWC5tfBUmQG8f4pUSYXS0+JDbsq8G7ajX0vIrUrg1xjHckAv5q8d4V3DhTrF0sq+nSzhwEAWsUURQxS1ccx5o28OKLLwIA+vv7sWPHDgDA+fPnAbTLd0lr6+7uxtDQEADg1KlTfI5Wx4tF16G/ZzIZnDt3DgCwdetW1jb7+/tx9uxZAO08StIuXNeF53kAdM2atCDDMFjjCcOQNZi+vj6+XxpTrVZjbefixYtMbrOwsMCaklKKz6GU4muSNinncW5ujo+dmZnB9PQ0AGD//v08Z3Nzczz+lQRpsVJzjaJI0wJpzi3LQsGy4DoO7Cji42neoijSPpMGnM1meV58z4Mbhrye9C/9PZvN8lzFcczXcByHtdTuINDmmY6ntZTaqNRoaV1t34dvWWgl46MxZzIZ1pqz2SzPjWmavPZxHEMByCT3utS6ynWia7daLa3qLeP7aLVaCMNw2Wq4FNc3lhW6xHNQrVZZKACLD2wcx/zwGIbBieW1Wo3NeHrBfN9nHoQXXniBv+/u7uYHtlKp8GfHcfglo5Jb13X5RSiXy9iyZQsA4Ny5c/ziXLhwgcchj5ebA5l/dE1AN2UbjQY2bdoEACzM9+zZw2MbHx/nevuuri4+n+/7PDeGYfCYaPNaWFhgs9IwDGzcuBFA+2UnV86RI0dYoE9OTvIGspLo7+/ne5frSvcvBSmZ8vlcDoU45nWjeY2iiOcliiIWfKVSib8vl8toGgYWlFpyXaVrZ2Fhgddk06ZNLHTNWo2FnVLqEqHlOA4LTAC8wfu+jzAM4YchGkqhk5vM930W3PIc2WyW597zPJhRBNM0NdfGUusqS9zr9Tr/P7nLyuUyDMPga6ZYf0jdCylSpEhxDbHsdiu1RHIHNJtN5jKVAZAwDFmDunjxIsbGxgCAg0W5XA4TExMAgOHhYdYoJicnNa2JTDbLslirIs2nWq1ifn4eQFubputFUcQakXQNnDp1irVeGnOr1dJMQbqG67p87YGBATbxSZu+ePEihoeH+Td0jXw+zwGVOI55PpRS/JnuL5/P8zjPnTunmeqkie/bt4+DZqZpYmRk5DKrc/Ugrcw0TQ5Mtlqty65rEATwTROhYbC2SUGyOI55TYrFImt/8/PzfN9uFMHMZGBZFizL4oAdBa1kkEkpxYE+0zR5jjzPg51YFNlslu+Bxux53pLrms1mkc/n4TYaKOXzqHYE3srlsrY+hE43Gmn/0oVEf8/lcryulUqFNfhisahZBnEcIwgCFAoFTSNOsb6wrNAl8yqTybCQpBcGaL8sJCwMw2DC7o0bN7JZTg9yX18f+3yr1armF6NzuK7Lpic96AD4ZVJKsZAYHR3FU089xeOj703T5Je5t7eXhQffsG1rUXMCmbd0DI1DRuPluUiYh2GosVEp4TOk8dPfM5kMm5W7d+/G8ePHeW7I1SDPMTU1xW6HlQS5fjKZjOa3J8h1BQAy5GV2hlwTAs0Z0L5/9tnaNozk73JjkkJSZonQOZvNJq9RlJj3NO5Ot4tt25qbg84hzXjDMLTsGLqGdKXQc2CapraujmHASnzFNA65rkv5iHO5HLs5aD7ks5VifSJd/RQpUqS4hlhW06VdulAosKYrzThDmJu2bTMJtOd5WoAKaJtXBw8eBACcPHmSTTAZdXYch7Uw3/eXDJaQiX/06FHWKDZt2sSatW3b2L17NwDg+PHjnFtLmlQ2m2WaSJmx0Gg0WNuTGQnkwpiYmOC/t1ot/l2pVGJTW/IHN5tN/kyBNhl4VErxOJVSnOtr2zZGR0cBtDV/uq+VBK2NdAfIde3Mc3WCANlsFq7j8D3ROaT14fs+PzP5fH4xqGaawOwsDMPQ1lUGN+n5qtfr7K6hwBVBat/0Wa4ruWXq9TprnrSuzTBEHYCVrAXlFs/MzGgZHLRW+Xyen0WlFIpRhFyzqWm65AaRucS9vb08d6ZpsqaukiBioVBAGIZaXnGK9YVlhS490LlcTos4SzNPZhaQyXf27FkWdiSQ6vU6v2xjY2M4deoUgLZAl35QEqS2bWspO0D7RaPfjY6OshA8ffo0F2FcvHhRE8B0TTlmGpNt22xukr+RxkTCgwTRjh07tHQiSleTKUQy7Uq+cJQhIIsjcrkc39eFCxeYtPvo0aPsduju7l6VKDcJ2lwux+eX65rNZnmOMpkMzGYTpmlqUXdaJ6XUkmP0PI/vz7Vt2LaNbDYL27bZDysFKq2lbdssEOWmm63VFlPQhOCmuZdjlhs5mfPFahXdxSLmk+NpbUqlkrYJ0L3I5plhGCKMIsRJqhr9luIVcl09z+P3plgs8phM04RKXBm+72vunBTrC6l7IUWKFCmuIZZVo0ibMQyDd3epHRaLRS3KTebYrl27tDJLoO1SoAyIM2fOaDmVtOtHUcSBIxmckCY5md7NZpNNNBnxj6KIz10sFtlUJYRhyON3XZfHJwM42WyWr01m/+7du3Hy5EkAbW2GruG6Lpu6URSxdp7JZPh8pDUXi0XWpMrlMl/jhhtuYM05k8lg+/btAICnn36aXRArCVkWu1QhglxXCnx1BiRl9oJ8NmQ5LBeYJMFGKvGVBST0r5w3mlt5TUcE3Xzfv6RlThiGrN26rqtpmADgJi4SoyM4K4Ob8jkPgkAr2PA8D2EQwBQWCll2pVKJzyEDfNJ6sCyLA5TLtYVKcf1jWWpHeuGjKGIB53keuxTiOGYf6/Hjxzm9Sab6kKnl+z6b2f39/ewPe+6551jo3nTTTXj22WcBtFO16OEm/6H0iUqQvwxoCwMpxOklIoEfhiHOnDnD90cbxUsvvcRpbrOJ/1Gi1WrxS9jd3c1ZDb7vsyvF8zzMzMwAAG688UZOpZKcDiQMpqeneVMrlUp8rGVZPFbJAUEpbCuB7u5u/HwQIBeGAGWOiKh7JpNZXG/fxx2WhQu2jRnLYoFJ9xRFEaqJoCkWCnx/QRCwG0E1m/jJTAbfTlKr4uQc9IwsZ2rTnP9vYYh/FpkHdP0w+a3n+8glz6UjfM9hUgl3h+/jh7aNRiIEaXUDUenm5nJoUdpcFKEkMjsy9TpuVApHhIAlSDdWtVpddMG4LhrJ82WaJv53AN9MNjQ/CPBYPo/TyWabYv0gLYtZp/ieZWkbWF74uvP5PMoUiIpj9DgOnnUcnHccLa8ZaAuk2URA97ouupJAVKvVQpUq3DwPW20bX080UBLcTfKDdmh+JDClv3gcwL8k14yiaJEUxzShADSiCCVR4Sb9v77vozeO8a+Og3IixOkaHhYFezGXQ01sCH35PFRyL7lmE75S+GfXvYToXFplM83mYsVaPo+FZGOxLAt7AHy9VEIYhmi1Wki9uusTywpdyepEZvquXbtYQzl37hyX9g4ODmoRWQ6iJC9CZySa/j4+Ps5a3rlz57iV+ezsLGu4pE2XSiU2w8lcBdoRanppc7kcv0SmaXL2AbkfJicn2UVx4sQJ1uabzSbnGY+MjGjly53zIbMeZDS7u7uby4cbjQZrwM8//zzfMx3b29vLY5bzZgltUr7MK41J00Rs24sZBErBTkxuO4rQTD77UYRJpXBOKbwg3Aw9iRYYAZhJBKNv26gl56vHMRpkLdg25qMIL3geHDEHYXL/gWEgSs4rg2D1eh2UKVs3TcwITZfWuJlco2EY8Mi9IFw7nmkisCzUTBPlbBZT9D3lGRsGnORcNcPgzcaPY+ZpUJkM+opF+EGAWceB3dEl2DFNqGQctUIBleR3M54HX+TyNsIQM7YNM5OBb9twkWI9YlmhSzt6GIascRw/flyL1pPPU5rywGJSuvQZkktBJrJPTEzw73bu3MmmZqFQ4IwEcgdIs851Xfb9ySR6OW6lFGc70PX27NnDLoXBwUEWxo1Gg6PmpVKJBT6ZyN3d3XzeKIo0Qhjy45XLZRaY+/fvx9GjR3msdD0qEMnn83zuRqPBqUqZTEYT9KuRvUBCTRLNXI6W0XEceI0GGr6Pqige8IUGR2O3LIu/r9VqLKCLrguvUkE12axkiiD9juYzm83y+ZpJilZykLauNHcEmYlBxwCL3BGW76NQKMASlYmATicahiFvCLZt87OYz+cxWCigK8mg6Ly253l87Vwux+eW6+o4DsKkSm+59u4prn+k2QspUqRIcQ2xrBp17NgxAO2dXtayk4a5sLDApv/U1NRirb3rchaC5FKQ2iHBNE0296emprTMA9JyKMPg3LlzHKzLZrNasEvmEZO5XqvV2NwnjddxHHZR3HjjjVwoMTAwwNkJhUKBNSVJF0iam+/7Wj4ulfDOzc1xEGx6eprHTXNQqVR4PPV6nbWjUqnE2pYtTP5KpbIqSfSSyJ3mW2rvhUJBY/SK4xixYcDOZLj8Wa6r1DClq4X5ESoVLqSRHBcSMo9a+nSl5knWSr1e5/nvZPwC2pq6XB/KMZal3LLcl74LgkCjnSQt1XVdGEEAwzDQarX4vqTFJ4s16PtSqaTlC1MZ9cLCwiXacor1g1TTTZEiRYpriGU13b179wJopyuRJjY8PMx+Sc/zWGtaWFhgn202m72EWGSpXE+gXcIriV/IRzw0NMT+QdJ2RkZGtPJh0kZlrqwkFLFEhJ78ta1Wi6u/zp49y1rXxYsXOagmNamlyEkkGYppmuzT3bNnD2t9Fy5c4GtTS5be3t5LmKsAXYuWvvHOdjUrhaW0d5mvKqPzURTBCQK4pol8Ps+apQwk0ngty9Jyq2Uw0nIcOI6DXC6nVbMRmDfXNLV1pfk3RfBMBmXpecjlcqyBElE4nddxHM4nluQ2nZDlz3KNKWXS8zz44hwyv5eeP/mMy1RGSl0jlrvVWNcUawPLCl0SFlJgnj17lh/0sbExnD59GkC7YwOZqpL+jgJSkuBblhU7jsP5sTMzMywQT548ya4EKTgpuKGUYqHc09OjZRaQqSmp9ehcR48eZbrGKIo4w2BwcBAvvfQSAD2DQLKGyWAJfZa93PL5POfTOomQARZzhHt6enhs09PTLKwlTaVhGBzQKhQKTCS/kpA5yzR2WXLcKZy6bRuDrouKbfN8SkpFmTlC6zo/P8/uADcRmMTA1Sn4JHuZLLbo7e1dFGYzM9q6dpZ3m6bJz1q9XufxFZLcYRJ6nT3S5LpmEvpJQH++PM9DXC6jFkWIO0rfAX2jj6KIi2Hq9bq2ro1GA9MJtaMk0k+xvpC6F1KkSJHiGmJZTZc0ji1btmjaF+Xsnj59mhmmHMfRyJ1Jy5EENuSKaDQaWsCFtIsbbriBg1nDw8OXBNKeeeYZDlpJ01gGSDp5UIk4fdu2bQDaJcOSQIfS0YrFImu9zWZTI1Ih0HeuSJCPoogtgnq9zu6KQqHAmpcs/aVULJmulcvlFvNKPU9Lf1sNSG2VNEKZE1yv17WecuQKCZTSymfp3khjlx10S6USB91ySsGo1bjsWHb7BXQXxeXWtdVq8XMny3VpnJZlsdYuA3CUz02ERm5ifUgXinRVkHVChOPAYsufKCkHlpp9JzKZjBZEluOPoigtiEhxZUL38OHDbJIWCgUWtCMjI/zi+EnTvc7P9IK5rsvnazQa/LLIF3xycpKj+1NTU9i3bx+AxcKGvr4+Lp6QgrVer2v1/5IInVweZPbHccwujAsXLrBAn5qaYrPw7rvv5nPTuWQtfqFQYOFSrVaZ1aynp4fvV94jfTc9Pc0+6w0bNmjl1HSPkhtC+khXEtJnTOsnXQrValXjpwjDEL5hoAVcsq6OqFKr1+v8u/3797OP3wkCDB87hr2bNyMMQ14TEta+7/NcyHVtNBp87p6JCWxOslzkPcjshqXiB4VCAa7rYuO5c9izYQMqHR0bJAl957pSsUwYhpr7Sj5f9K9kaJN+Zir8IdYyYiRbraKXFK99pO6FFCmuEMbLH5IixctiWU2XzCTbttm9IM3tiYkJrbpJ5vJK8nKgrRGRNqeU4hbtH//4x7Fz504+pzStZb4m0NYoiAj9SnH48GHt/7U2NB2tZmSfsleCW2+9FUA7F5hcCq1Wi89HAZnOLrZ0r7Ozs1x9JyvDRkZGOLNjJSHblnfy0gI6AY1pmvCjCF5SFkv3QmPM5/PslnFdl8vCP/zhD/O6Gs0mur/wBdzx3vdqpr/MdFgqm0ASHI08+ihuffBB/lsnEfrlmLtoXfv++q/xpnvvhUqyWOQ1OucFaBMxfeITnwDQzszJujnuo5QAACAASURBVC4yQYCsuIYMtNH4O10Ksrt1ybaxsb9fyyNOsf5wRe4FWcIrfWFdXV2a74x8boODgyxEJA0emV07duzgrIdt27bhxhtvXLEb6sQrFdJXA8owOH/+PL9kkgNCCnr6bm5ujn2C1WpV8xOSm6PVaq1KPy3piiG/q1zX7u5unZIw8dUa4l5I4Eh3juwAoa1rowFs2IANSWv5q8LICEZezVpu3IiNN94IJK6xl4NstPriiy8iajQQxzFy+byWMUGQvmWZ/kdKiVIKQavF6YgpteP6RepeSJEiRYpriGU1Xc6zdF3WiDoLDuhzFEXYs2cPAN3MlsUHN9xwA4B2Du711IKa3DCyZVGxWNQyHAC9HczMzAxruoODg5y1sXnzZk7y7+/vx65du1Z8vK1WC11xjB7XRTGxYCT6RRflOI7R1Wqhz7LQH0UIRGDRAJD1fRQpo8H3USIinIUFICmxRqMB1OuL/78cLqcBNhpAwlV8VajVXtHvs5UKRon72HFgBwFyUYRNSbEFsLiuVjbLzGN2FLF7JpsUhQBJPnOthrheRybJ4Z41Ui/xesSyQlemYZGwUEqxUJifn9cqeGTDRxK6JGinp6e1KqbriWWJUuEMw2D/ZhiGGlsYoPO83nDDDTxfGzZs4NQ23/d5boIg4GNWEkop/Izvox9gesFYpG3V63Uep2EY2Od5GDRN7F5CIDphiKJoMz6YENV3/6//Bfzwh0huqv35r/7qyga4lDA6ehT40peu+B4vwZNPAnEMLLHJLIXCxYt4XbIRbmw2UcxksCsMYYpef5Kfw0x4fVX7D/x3GaO4xXXxvoRkP4oi/GUHL2+K9YGUxHyd4q8T4iLy1cvqtDmRRmWaJv6PKMKPTBPHlxC6edfFUOLTDsMQO5Jqvzv/y3/BRgpINptAGAK//utXP+CZGeBDH7r63xsG8M53Aknq48th7uhRfDXZNI7V6xjwPLyp1cJfiIo6Wa4slZKl2hAZhoGRVgtfKJXQbDbTxpTrGFdEYh5Fkaa1SdNTQvLbkqb0zDPPAAC2bt3KRQTj4+OXlNmuZZBLQfKqBkFwyYslNZ9z585pAo9cFGfOnGHroNFocPuflcRSPd3o/wGdJJzuxTcMKNvmvGYZPCPLJ4qiRXLw19q6vkJTPo5jtmA8z9MCiEsJTGmdyIyeTn4G0zQ1IZ1i/WHZlacHo9ls8suZy+X4oerv79cIUEhAdHV1MeUjZTTMzMzwQ1yv1/l8q119dS1AQndhYeESSkRgcXOSL2upVOL5ME2T/bvz8/Ms/M6fP88CeCVBY2w0Gtq60noPDAxo/mizXIZpGMjn85wSRkKoVqsxZ0UYhkwstNbXNYoidpHNz8+jaJpoRBE8UYQhKzAJfkKWDrQzQigW4vs+GtPTmA5D9PX18YabYv0hzV5IkSJFimuIZTVd0sxM0+Tdu16vswlZSprs0bHSRKZCCHI50I4PtDvvytY9ax2SEJu0H9liXrblJg1REsCfO3eOtapNmzZhamoKQDurgXJFVxKyhJbGKYnNu7q6tHV1XReFxLVA46fiiFarxetuGAbnLK/1dSU3AH2m0uJ8Ps+uk6XWVXJYVKtV5rYwk1ZH+Xwevu/zO5Ri/SHVdFO8LF5j3tkUKdY0llVHZFUSaS6ymiaKIs2/RZrB3r17cfHiRQBgTS1OGJqAtn+LtATjOshVXIr8JZfL8b1JXy59NzY2xhpyuVxmTbOThJ0shpWEJHyX6yp90NJf6TgOso6jNWuUwTPSdC3L4rzstb6upmmyXzuTycD0fZhKaetKay3LmKktENC2HmhOqb2UZVkIgkALYKZYX1hW6NILT8xeQNv0JDrE0dFRPiabzbLgkJkO0pSlYJFpmsz09Up5Dl5rePrpp7mTxtjYGG8mrutyLzZH0AnK9uI0X7IM+Pz581p2wWrkM9NGWK1WOaOku7tb6/RLwjibzUK1WlBKoVarcWCU3EWGYWhE9TJPea1DUj6Wslm4CccCrQndq2QNi6JIew/oHL7voxnHmPV9dHd3a+62FOsLqXshRYorxNrW3VO8VrCspkumYhAErMHFccwMYQsLC5wmVigUOCgjy11l6tH1lJtLMAyDtb/JyUlOmcpkMlprHqBdLkxakGRiU0ppZdGS43Y1AlJSi6U1kSlj0oUUxzGspCIxKyoTJdl3Zwscuqe1DMMwNE7hrFLIxDHfN7B4v9KVIpnF4jjWy92ToNpqrWuKtYFlV17SMsouCGQ253I55gY4duwYm1uFQkEzq4C24JZEz0u14V6LME2TBa3jOOzPm5iYYLJ3mkfLstj/OTU1pblhpOuFBJacs5UEmf6u6/LGYNu21tZc5lHbtt2mJhQMW3JdJcPWUmTkaxHSp+s4DhylmIRcUjoCOrNYrVbj7w3D0NbV8n1kMplVW9cUawPpyqdIkSLFNcSymi51LD158iRrMNlslrv3Hjt2jHf4vr4+Npl83+f8RII0oeM4XvPRbYLMynjhhRc0xjEKONJcbNy4kXNwh4aG+Pt6va5V9pEm1dvby5roSoJybKl3GH0mDbvVarEZXSgUYFWrsCxLa8Mk3Qdy7JfV4NaYu6GzR1ozITCqVCq83sR7nMvl2HIbHh7mzI56vc7vDfWB85RCX1/fdWPppXjlWFboSuJy8lsCi3wKcRxrQoReSNkCW/p2yVxTSmmk2WsdJGjPnz/PAnN+fp77vVH2x5EjR9jlYNs2m56SLjOOY/6+UCisyjzJXnay2aYkXZemdRiGCC0LjSC4ZF0dx9F6vdHvtE11jW6w0oUSJcURspvFUrBtmwWqdBXFiT8477rI5/PXFbVpileG1L2QYv1gjWnbKa5PLKvp0i49MTGhtRYnzW52dpZdEC+++KJGkEO/la1NaHe3LGtVzOYfB5RSrO3v27cPp06dAgDs3LmTXS9kbo6NjTFvLrAYPOvr62PSGACsIctE+5Ue8695HrqF1pbJZDTt1RQR+M2VCvYZBg6J4+WxmUQjNAwDA0nX5ZFHH11sjROGwL//O/BqCgK++U3goYeu/vdPPAGcPg2I8uzlMDg5iXcnfLpzlQq6DAPbwhCjwioJiEEsDIEk0BzNz2t52VVhId5jWSg5DoxaDVG5jM8kgecU6wvLCl2KZu/du5cj3oZhsC8rDEN+APv7+9kFYZrmJc0Hfd/XfH/XS+25YRhsrrdaLeYeOHnyJDNy0cZUr9e5MCEQpnqr1dJMe8oUkelYKwnf9/GIYcAtFNiFpJTSqtBozI7j4CdaLTxtWXhmiWKNbDbLxwLA+LZtAIB9Dz6IQSp8abWA//7fgd/+7asf9EMPvTqh+2d/Bvz8zwMJ38XLYfroUXwp2UCPHDmCvlYL98Yx/jLhUACAbFLgICk75TthGAaiRMibpgm70cB/S/h4I8MAkvcoxfpC6l5IkSJFimuIZTVd0kzr9TprOLZts5ZaLBbZ7XD69GlN45Fk3oBOmG1Z1nXTgto0TdZoZTnvhg0buHCEAk31ep212I0bN3KhRLPZ1PgLmiKJfjXhOA4XtEhu3VqtpgXSTNOEbVnICq6GpdbVNM3rovwX0FnGbNtGGIaIogiNRoO/JxdZGIYaXwa5kxqNBq8rnaPVamnacIr1h2WFrqSuk+Qs0s9IroaxsTGO9k5PT/PLTNH6IAg0opynnnoKQDvtjBparkXs2bOHW40/++yzWpoRFU2QEO3r62P3gqwAK5fL7JqJoogpH2WvuZWEJNCm9LFms8kC03EcLfsg47rIOQ66RCaFdBvRGLu7u1kgrXXI4ohSqYQ+x0FXvY6hQoEVBlkcQe8HEdoAbUErq9ay2SzTZqZCd/0idS+kWBtIhVSK6wTLaroUXZdJ8XEcsyklG+8ppdjVUKvV2MQaShoBTkxMsBbUarW03lFrHXQP1AOL0Fku2tXVxXNQq9XY9Ozp6eGuv4ZhMC3mwYMHmQVsJUHr1Gq1tPWj+1BKLRnoXKoFkaQ1lFSRK14G/GN4Tmg+LMtq/yfazwOL+c5yXWXuM7lmOpHyLqxvpJpuihRXgutAOUjx2sAVBdLCMNT8fQSZ9iS5Q/P5PPu9iG933759eO655wC0Gx8WrzBfci2AtL4wDFkL8jxPC6gA7cALaTmFQoHnVPrJwzDE7t27AbRJcVbD302+Srl+siRYlnF7nofINBEA8OKYtXep3ZNFJM9xPUCm0LVaLQTJv53rWiwWtfuWJPHyeyrxlhZBivWHZVdetv+WUWsSrlKI2LbNFIalUomDNSR0ZVBBlg9fDwEFejnlC+m6LhNVS7cKmaSDg4P8/eDgIE4mifie5/GGZFkWE6SvJGhdVcKcBeibZrFY1NY122win8nAiWO+P1pfWc4qg0hrfV2lK6XVasFuNOD7Ppx8/pJ1bTabvEnJzJwwDNmVk81m4TQayOfzqFarWll9ivWF1L2QIkWKFNcQy2q6lEcqeVJlsEimymSzWd7te3t7MT8/z98D7Q7AZJp2dXWxtiWJr68HkGkpq/JI8/F9n+/3pZdeYo3p/Pnz3L4IAJcSm6aJ7du3r/gYX25dO/uD2UHQdh0k/dLoe0DvC9fZZ03DGtd8DcMAkh5nBPrcWTkoOwPL40l7TrsBr29ckWNJElXn83n+3Gq1NP8VvbTT09PYlpSDkplaqVQ4Z7dcLmP//v0reBs/Xsi5kZ0y6MWSJjltQpVKhU3PkZERPPvsswCAXbt28fnCMMTRo0dXfLxLkaQXCgW+rud52uYBwc9QFGWtdA7ZDWNJt8IaDELJTSiXy6FkGMh19ABcal0bjQY/867r8obkeR43ZA2C4Lpi2UvxypC6F1KsH6xxbTvF9YErytOVEWqpwYVhqLVqoQqsOI45qEbo7u7mMuFyuYwjR44AWPsBF0CPcpNm02g0LmEIq9VqHEibnp7mYEo+n+e5iaKILYLz589zJsNKQhJo02eK0NMY5LoGQQDfNNFUirVayRlLkK3FX3Pr+gq1bRlIo3WN4hjNZvOSDI56va61tpJBSJovantEGvFrbn5SXDMsK3TJlIzjWOsKQQ9dHMcauxJlKsiorXw56QGM41hjt1rrIKErm296nsdCl/ygLUFt2NfXx8URL774IgvXixcv8tzcdNNNq+LzJs4ApRSvqxxvFEWa4IiSdfZFe3ESyplMhoWP7Law1tdVsq4xeXkcM3cCsOg6k11SZAcVSc0ZRRGMxCecTZjGUqxPpMmC6xTvqNdRjGPNjyuFDGVjO1GEA0GA3baN2+IYjeQYt9mEoRRs32cBq5TCUJLi1v/FL7Y5cIE2n+6//RvwyCNXPsBOof3v/w58+tNXda8AgO99D5iaAgTvxHLom5jA206fBgC8oVxGPggwHgSAUogSgWmRhRNF3J49X6sxv7Blmqgnft84jnFzHOO95TJipaCUwheuE07pFK8MywpdIkMpFApsCm/YsIF3dplvW61WsWXLFgBt05q0XknkTRpfLpdjkpgDBw6s6A1daxw7dgzPP/88gPZ8kRtGKcUl0DRHruuylSBzYXfs2MGlv7JgodlsrkqxQalUwvfzedSrVV6nTCYDU+RiZ5NATy6XQ5zN4mQmg2eCAAuJ5lZMsh6cTIaFrmVZGE/65/3k296GTckaw/OA+Xng3e9+ZQOVLoGLF4H/+l+v5nbbiCLgZ34GGBm5osPnjh3D1558EgDwfL2OgTBEo9nEX4sedrQ2KikTVgC6RUmwbduYSrTiIAgwEkX4YuJeME0T/hoMMKZ49bgin26z2eRuBr7vc6qTbdvMRBYEAUdzpQlGAiSKIi297HrqEUXpcaZp8gvZ19fHgnQkedGnp6fZPB8eHtZSjiiabVkWC+MwaYa40mi1WmgB8AwDtWQdZDTdVwo2pT3FMeaDAFMAJsIQ1WQTrSR+fUeMPZ/JwE9cUqq3F0gI3eF5QD6/+P9Xg1wOeDWdFvJ5oKen/d+VoKcH9eR5rVoWMlGEpmGgbBgIk3WjZ9j3fdZ0M5kM4uR3cRzDSwRwaNsImk00MxmmiUyxPpFmL6S4IqQ6WYoUK4NlNV3SYMrlMk6cOAEA2L17N7sMnnvuOSbw9n2f3RG+77OmS4GETCbDEXqjI8l8LcMwDHa9DA4OagxTkocBaFsGkqyczHLZysgwDNZ0a7Xaqmi6sgU7tRcKgkAL/NHYPM9DvdVC1TRRF4FTua6UwdDJsraWIdcBAGunsnSaLMEgCDTeXJn1QOvqui7sIIDruqjX62lxxDrG9fGGpEiRIsUawbKaLqV7+b7PFWZnz55lv+TWrVs5aNDV1aUFGEhTor9ns1ktUr4aHRF+HFBKYceOHQDawURKCwvDkFPuSJuM45hzmWX3387WMKRFmqa5Kl2Tyc9uGAanezWbTdbQCoUCr2UYhshkMnBtG7kk6g4srqvsItxqtbiTyFr3WUYiYEYpk7ZpIiOebZq7YrHIayZJomSZtWVZnAttGMZ10w07xSvHskKXXs7Nmzdz6/D+/n7NnKRAWk9PD5tSsiRYUgASJHXgWkccx0xZuXv3bg4mytxmyahGbpp8Pq9xHXAkXClNKK4GBaB07cj2QiR0c7mcRuDtWBaymQzypqmXB+PSdZVk92sdUuhmstm2eyAJhAHQUuXocxzH2hzJdaVgs2EYGkVqivWF1L2QIkWKFNcQy6pRZBafTpLEgTYBOaVCnTlzhlmwgoSJCmhrSmRWURuaRqOh5bBeD5oQ0NZsSJs5fvw4l/CWSiUOWJFrxnVdzmWemZnRXAqkHdVqNXbfRFG0KsQoFMwjAhag7UagNalWq3xd13VhxzEcx0HBdXkNyYz2PI/dUEqpS7TAtYo4KfkFkgq9XI6ryWitSBOu1WrsTpE8yZJprl6vs6YrA2wp1h+WFbpkKm/evJmT6I8fP87+qLGxMfZNTk9PY+vWrQD0/FJ6CU3T5PJWyU621iF7w+XzeRa6tVqN83PJNdNqtbBz504Aer2+YRia4JL5n6shvCTLmOzfJtnjZDmzSgjo4zjm48ktYVkWZ6V4nnf57IU1JoTlBlKv11GOIjRaLZRFV2tZ7kvfda6rVDQsy+LNbK1vSimuHql7IcXq4zrZYFOkWAksq+mSdlssFtnklVrQ0aNH2VyWEW9ZmUVwHIfdD5IY5pXi2LFjAPRAjdSuZLdiwzC43PiV4MiRI3wOOWZ5TdJUnnvuOZ6D559/XiO1IUtBBmSou2+j0VjSxSKDkDILZCUhg5uk3ZZKJdbcoijisYdhiNB1ERgGPMNgTZfO4TgOa2+SzPvMmTOLZnYYomdqCjPHjrV5HTqCSEt1GQba60r/Pzg5idlk7SWRjJyfpYJ7FOTqe/FFVJ55BnHiFqF7JQ2eIHPQyfXSbDZR9jw0owi1pOQX0Fn46JpyXeXYaF0t8fsU6xPLCt0eUTJJRRCWZfGLV61WmcJRljb29fWxK4FevGKxyL5h3/dZoL/lLW9h8zSTybAPOI5j7fpAWxgQJaTsRXX+/Hls3LgRQNvXSCb8xMQEuzxozLIrgmwc6Hkem5MXL17klLbepPRUKcVZCEEQsOugWq1qdH20OWUyGRYuMhOABLDv+zw3jUaD/YcDAwN8vlKphMOHDy+xMq8OJCxc19VMZLp/QCcpbzabqHke6tks3xOlwyml2L+bz+dZUD3yyCP8DORsG29+6in81ZEjUErxutK8hGHIQj4IAh4HjRMAfuH4cTyePD+O4/BzJzd36SOn8fu+jyAI8PPT0/jWP/wD/CROQesqC1AMw+B1KJfLvEEahtFWFOIYlmBVo/Q4WbotCyVkVkMQBG0fepLdkJKYr1+k7oUUKVKkuIa4oiRQ2TK6VquxVuJ5nqaVkRZUr9fxwgsvAAAT5Rw9epQ1l7GxMe4D9txzz/G5M5kMa5iGYXD2BGmpvu/j/PnzANqaCmm3XV1drPUawgQeHBxkdwRpPq7r8melFGs50uVxww038DhIu+3u7mYNrdlsor+/n+eGyoClmSlzbGlepLk5PDzM2r7snpzL5dhFcerUKdaoVwO2bfPYqtUqa+me57EmViwWkW02kbNt5IVmLPu/kWYXBAGT/ywsLPCxedvG/ulpHEtyVGldae5lSawM7pHmCgBzCwvcusgwDM4MofnO5XI8JsnrS1kZE0GA56em0EjImqSmS9qtzFVWSmlraRgGDOi5t0vlUMvybyIupzGR5it7C6ZYf1hW6EpzkwTG/Pw8v5yGYbCw6O/vZ2F8/vx59qUS7eH27dvZjfDMM8+weSp5XGXPLlnpJP3J9LDOzc2xsBsbG8O5c+cAtGkSadyTk5P84lLGRXd3N7/UsguA4zh87AsvvIDh4WG+DtDuaUbNI48ePcqCUfroOps70vhps5H3Kl9O27bZVK1UKly1tmXLFm0NVgphGGIgipCxrDYDGIBgdhZRMs85w8Bwsj69vb3YBCDIZmE6ziV+6Ng0ESX3V6lUeF5c111sYgmgK46xMWEkKxFTmWClo+coVApNmhfhn++1LIwI3ofeZG6LybqWSiWez2aziVoyDtOyYDoOBpXCFstCNXmOc9REFUAj+a7aavFaOY6DgDZkpTBgmuiPY4wqhYzonQbo65rL5RAIcvNSMk+RUugxDGwxDGQsC65p4uJ1wlOR4pUhJTFfp7i92USXCDhVwxAcAlQK3YlQKto2bmy10B+GGLXtSwJ7MhDVDAL4yedMEMAism8A26II93ge7CgCeWqbiWD0fZ+bPlK3XKAdgCOhOxaGuCfZIIh4BgCyzSYUgLzwSfu+z4LbTKroxqMInlJoJucgj2ocRfCS6zXjGBkS+EnKmErG1KUUxpXC3WHILw2dQ/pxM4aBiMYRhsiJCr2tSuFNcQw7DOF4Hv5f4bNOsX6wrNCVwQHSxGzbZq1RaqZKKZw8eRIAsHPnThw/fhwAWDs8duwYm8r9/f38eXZ2VmtRLkuJ6Xv5otMLSXwHQFuzJjeGaZps4o6NjfFn0oJefPFFPl8Yhqz1SvdIX1+fFtyj8Vy4cAEAsH//fnaPbNu2TZsnOp9sZUTBQdu2WZuemZlhF8Xw8DDP4/PPP8+/y2QyeDHpxLCSsG0b/9TTo5Xthvk8guT+gyDAUKJ5dnV14a5WC887Dg6HIZvidB+u6/IceYKkPZPJLGao+D56AXy5VILruvzbamIhNBoNJk33PA910Q4nlzwPo4aBxxPt2/d9vibNVavR0POPk++7ElLxbL2O/y+KcDEZk5ncd09PD4zkvLVaTQsyEnzfR6leR+T7eEx0+A0Ee1w2+V2pVOI5qFQqiJNj4zhGr2Hg/7EsQCk4SgGJpp9ifWFZoUu7t+d5bOJLs1EKlnq9zuTmp06dwt69ewEsCpwNGzawH+3EiRMsBF3X1ToPEGSTR9lGhszQZrPJ57Msi4s0isWi1nus039YrVa1mnnpCybhOTs7y9FxGtPOnTv5XoaHh7mYYXJyUitsIEihI9PjJLk7/a7VanGkfHBwkP3JzWZTi+CvFGRbcNk+XSbuy3E2m03UfR8Vz2MhLd1A5Pu0hSYstVEraeNO8925rp0pY/S9TBmjHmN0TRqrFHCEzjQwx3Ha5DOOg0zHmkhyfUlCEwQBX4/8u0ZybUk+T3+XzUnl8yw/ZzIZFAsF7V5SrD+kTqUUKa4QqZhMsRK4IpYxz/O0gJMMHMl+XqQx7N69m6PL9K/ruqwpAtDaj5NLobu7mzUv2b5cFhdQ3u3hw4e55DaXy7E7IJvNcoaDZVmXlKVSziWdT0aoaRzkqpBzQOxQQDv/l8a5efNmnD17lq9Hc9Df36+VgALtoBzlseZyOXZFXLx4kX/X1dWlmcmrQZFI2qrneRrBulxXCoo2Gg00Gg3UDAOtDm0T0Ck7O/kbeF27uhA3m1x0Icnbgba2KUncae0lz0Gz2USUrHGxWLzEtyzzjGXmAeVl25aF3p4eRKLYBWgHhul3vb29HDidn5/Xz2HbCMIQjuOwRUf3KtdVsss5jsMBY8dxUCyXMTw0pGn7KdYfUk03RYoUKa4hltV0SRuQZMzSzxYEAWtzUsNcWFjAWNIVVjIykc9s06ZNfI5KpaLlfNJ1JLkKaUTSdyYDWyMjI6xtTU1NsSbSbDa5WoquMTg4uKSmq5TiYKHM1yRNva+vjzsXU1Uc0PZPU0BMWgQyyEhajeM4rJEPDw+zxjk1NcWa/7Zt21hzXi0+3aXW1bIsvmff97WKQqvRgG0YcEyTNXK6z3q9zusqA3OSYYu0P9d1EcexpmkTZO6rLIOW7Y9Iu5Vk8XTs0NDQZRnO6Hjf99FMjuks0Qb03OL+/n4tj9j3fQRJDKOTzCmTyfB8yTJn13U1fzewqJGnmu76xRUJXdu2tWCRfOg4AT6fZw6CfD7P5iedo9Fo8AvSaDS0LAWZKC6FLpmZMihCjF22bWN0dBRAO3BHxziOw2OSZOlk5knycGDRjVGr1VgIlMtlPp42D9d18cwzzwBouwBISPb19fG9SCEuxy/7idGxTz/9NI95eHiY6R89z+MXePPmzXj22Wex0pDrSgKzs1CE1iSfz7fXyDThCBpL6f6R7GR0jmw2q20YTiJ0oyi6hDdBugNkIY58NmTZbKPRYPeOzL+WhQg0jkajwSXecRzDS65Nv5fPQr1e5+BsPp/nDTeOY9hKwUpK3eXGSvdK42s0GktuFPLf1erynGJtIHUvpFh1pCSGKVIs4ooq0mZnZ1kj2LNnD6c3bdu2jTWUhYUF1hQnJiZYmyJzOp/Ps2YQRRFroNVqlTXkarXKLoqDBw+ylkOmoG3bnALW09PDJbrDw8OaJkRBDaBtdgLQOhXT36Mo4sqzgYEBDqKMj49rGjCNgdwIsi+aaZqcXrawsMAuiLNnz/JvSXuanZ1l7banp0cjjyFNqNVq8ZhrtRqXOq8kZPBMVgbSeksyoSAIYGAxAEkaGv3Otm1tPiX7GHfCzWZhJhqgbdsaQRCwuL5Aez7p77lcbtEiQTnoewAACGxJREFUqtd5vmS+M415ZmaGK/mUUhy0zWQybcsrjuFms8iJtjp0rNRCSVsOw5Dvy3VdbOjvR08SHKTjZQ63dM3Q36WmS26FTpa2FOsPywpdEjzbtm1jk3d6epqFz8LCAh+Tz+e1HE1JDQjoLopWq8Xfy+R6yXo1MTHBHSrIxPc8T3tZlnITtFot7drk0yW3xO7du/mzpKwMkvbYdI7OKL1s3Og4jkbOvnnzZv4d+Xtvvvlm3pzoPmQL9mw2ywJsYWFBe4EJspX9SoLMX8uy+D5lRF0ppVFUWrYN27KQAbSuFjReMvFbrdZlKRdps1NK8fFLxQnk+IDFdfU8DzVR1EJCn4sjWi0em2VZ/KyRmydMNnqVCHHpDuBqskxGyxemYyzLameSJP5omUdMf6d1c12Xx9y5rlEUccbIWm/cmeLqkboXUqRIkeIa4or4dDOZDEfU6/U6uwz6+vp4x37ppZc4a0BqSrIslM5XqVQ4U0BmMszOznLZ8IkTJ9i0pvNOT09rFUoEafbOzc1pgSsqA77pppsAtKvUKA+Xqq0AvfdbJpPRsjIAnedVaroy77fVavH4aR7o3EA76EOVc1u2bGFTc35+nu9Rmq+1Wm1VesldLmNBaqlaf7BMph0Is+1L1lXmrZqmyZVhsiINaM/1fBL9J02W3Q+uq5VPy866dL1Go4FyMibHcXhO5fNAz5TruuyimZ+fR7VahR8EqNfroHwJua6ShEgS8dNzHgQByuUyGkGAirgm3Ucmk+E5zefzHGSdm5vTLBt6Tuv1eqrprmMsK3RJQLRaLRaAp0+fZtPN930WakNDQ+zblAniMm2KTP1MJsN+zrm5OX5Btm/fzibi+Pg48xvQwy2j6mEY8sPd29vLx7iuy66QbDbL56Z76erq0pik6F6eeuop7Nq1C0DbP0hCgIRrqVTiF6VWq/FLJqP0N9xwA1+n0WgwvwT5niWt4czMjLY50AsuGcdk4cFKgkxzyakh74MELo251WyiYZpoAJesaxRFvK7ZbJbXNYoi7VhJiUgmtxTgsryb/O+9vb1aJkO/UAIkGTygM+KFYcjPJTWEJOpOuibNfalUYiFaq9W0jYBAPuQMAEtkcCw1ZtM0eSzZbJbn2rIsqEoFcRyv2rqmWBtI3QspXhZp9kGKFCuHZTVdisR7nsfaWn9/P2sGCwsLHBQ5f/48a2syB1EmhZMWK7+XAbFjx45pZDoU5CFNikxKoK1RyGNlUIO012w2y8EsGtuLL77IObH5fJ6j3D09PVoQq7NnlyRzkXmgSilmAhseHuYg3fj4uNYyBmhrUhQUnJiYWJJ7V0b9JSn6SkIyw2kBM9H7SwYprSiCbVkwBRmNnCu5rvQ8dJYHy4yDzoIPSXgj50K6GkzT5HWVbiPJSbxUYFU+R/T/8l+ZeSBziCWIYMcKAljiXPT8ycCYHIdhGFqeMbVwp7+lWJ9YVuieOXMGADA6OspmcaPR4Bd1YWGBU67Gx8f5wVtYWGAhTaa8ZVn8u2q1yua77D0m09Hy+TwLMzLjyuWyVrlEfdsqlQqbxJ0vGT3khG3btvGLderUKR7TwMAAZz3IqD5hbm6OBYArKAyr1Sr2798PoO1GkOxbTz31lDb+7u5uNkNHRkbYPTIzM6ORYNPxgF61tVIIggDvqVRQDAK+/7he57nN5/MaD8MOw8C8YeCiUqgn46FZNQxjcZP1fViJT5fMagCIgwBviCIUqUpPuKHo3zj5Tgpdr1JhU/12z0M2EbSmaXI6F60T+UwBvdiCepvdpBT2+j7TRjK/hWAnsywLUTIf0iftui76XRfbfR9dYoOwBWevlQhXyzDgJX9veB5KInXt9WGIrDj//+zoAZhifSAlMV+n+FxXF3zfR1mUdJOvdaCnhzcr0zTx07UaTmcyeKLZxFSyuXUnWqdpmpzKpZTi340IqyRsNvGhZhP/s79fqz4kgRqGIW+EUtOXnSj+zzjG/51s/I7j4CWRUwy0NzZJjESbn+d5CIIAD/g+vp7LYUpYWAA0Ah6Zxuf7Pgvx3nwe4z09+KlaDf+XSK0jzV12RM5kMryxzvo+NiY+7kKhgAeVwv9IYgyrsZmmWBu4IhLzU6dOcYCkUqkwu5dpmqylbt++XcuPpZdBJp7LVt30Ukh3Rb1eX/IzuRVk1oM0UTvLlKV5StokBclOnDjBhQ2SFatSqWilydItQv/KpHc6NpvN4umnnwbQdi9IU5XKlGkuisUi37cs4CiVStrc5YVAWw3uBemWIcgiAWkt2LbdFopJlkbnugLQ1pV+12q1Ft0YUcR8up3FCHTPlPUg+SmkqyjjedpcdLpupKvLsiyt7xldSxZmEIwOdwEJUs/ztH59xK5XyudZYNKGIIOQ0l1TKpW0PHDKXpCMbinWH5Z9o8l8P378OKdT5fN5FrSe52H79u0AdJeBFLoyNaazEgloawsnTpwA0Da56UEfGRlh3yu5KObn57XUInqhpJCU55eVb1S0MDQ0xJVnFy5cYGHc39+PH/3oR9p9S3Q2PpT19XT89PS0llBPQo1cJoVCgQWx7/v8whcKBd4c6KUE2sKsk5pyJSCLI+hacp1kehy5D7ykyEBWbBFkNSDNfbPZ5LFnTZMFjmEYlzwbkouAovud45OE4FEU8TVl5SD9TlaC0T10gr7L5/Pa32nMjuOwwFRKIYwiqDjmTQBYbMEuNy9JdCTX1fM8xIK2MhW66xepeyHFy+KfXLedwbBEkOlKEAJ4NMnlvVr8j8S1cLX4guPANc2rfuDnLAuPvUof7J+8yjlIcX3AUJ08eClSpEiRYtWQ5ummSJEixTVEKnRTpEiR4hoiFbopUqRIcQ2RCt0UKVKkuIZIhW6KFClSXEOkQjdFihQpriH+fwTBdxkZYmBvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "import mermaid.utils as utils\n",
    "import mermaid.visualize_registration_results as vizreg\n",
    "\n",
    "I_0 = torch.from_numpy(I0_syn)\n",
    "I_1 = torch.from_numpy(I1_syn)\n",
    "phi = si.get_map()\n",
    "I_W = utils.compute_warped_image_multiNC(I_0, \n",
    "                                         phi, \n",
    "                                         spacing_syn, \n",
    "                                         spline_order=1)\n",
    "vizreg.show_current_images(len(si.get_history()['energy']), \n",
    "                           I_0, \n",
    "                           I_1, \n",
    "                           I_W, \n",
    "                           phiWarped=phi)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVF scalar momentum\n",
    "\n",
    "Now lets switch the model to a **stationary velocity field (SVF)** with a scalar momentum parametrization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading parameter file = test2d_tst.json\n",
      "Overwriting key = use_map; category = root.model.deformation; value =  True -> True\n",
      "Overwriting key = type; category = root.model.registration_model; value =  affine_map -> svf_scalar_momentum_map\n",
      "Overwriting key = name; category = root.optimizer; value =  sgd -> sgd\n",
      "Overwriting key = nr_of_iterations; category = root.optimizer.single_scale; value =  200 -> 50\n",
      "Overwriting key = rel_ftol; category = root.optimizer.single_scale; value =  1e-07 -> 1e-07\n",
      "mapLowResFactor = 1: performing computations at original resolution.\n",
      "Overwriting key = use_map; category = root.model.deformation; value =  True -> True\n",
      "Overwriting key = map_low_res_factor; category = root.model.deformation; value =  1.0 -> 1.0\n",
      "Overwriting key = type; category = root.model.registration_model; value =  svf_scalar_momentum_map -> svf_scalar_momentum_map\n",
      "Overwriting key = type; category = root.model.registration_model; value =  svf_scalar_momentum_map -> svf_scalar_momentum_map\n",
      "Using map-based svf_scalar_momentum_map model\n",
      "Using default value = True for key = use_CFL_clamping of category = root.model.registration_model\n",
      "Using default value = True for key = use_odeint of category = root.model.registration_model.env\n",
      "Creating new category: root.model.registration_model.forward_model\n",
      "Creating new category: root.model.registration_model.forward_model.smoother\n",
      "Using default value = multiGaussian for key = type of category = root.model.registration_model.forward_model.smoother\n",
      "Using default value = [0.05, 0.1, 0.15, 0.2, 0.25] for key = multi_gaussian_stds of category = root.model.registration_model.forward_model.smoother\n",
      "Using default value = [0.06666666666666667, 0.13333333333333333, 0.19999999999999998, 0.26666666666666666, 0.3333333333333333] for key = multi_gaussian_weights of category = root.model.registration_model.forward_model.smoother\n",
      "Using default value = rk4 for key = solver of category = root.model.registration_model.forward_model\n",
      "Using default value = True for key = adjoin_on of category = root.model.registration_model.forward_model\n",
      "Using default value = 1e-05 for key = rtol of category = root.model.registration_model.forward_model\n",
      "Using default value = 1e-05 for key = atol of category = root.model.registration_model.forward_model\n",
      "Using default value = 20 for key = number_of_time_steps of category = root.model.registration_model.forward_model\n",
      "works in mermaid iter mode\n",
      "Using default value = False for key = develop_mod_on of category = root.model.registration_model.similarity_measure\n",
      "SVFScalarMomentumMapNet(\n",
      "  (integrator): ODEBlock()\n",
      ")\n",
      "Overwriting key = lr; category = root.optimizer.sgd.individual; value =  0.001 -> 0.01\n",
      "Overwriting key = lr; category = root.optimizer.sgd.shared; value =  0.001 -> 0.01\n",
      "Using ssd similarity measure\n",
      "\u001b[31m    0-Tot: E=013.2236 | simE=013.2236 | regE=000.0000 | optParE=000.0000 | relF=  n/a    | \u001b[0m\n",
      "\u001b[34m    0-Img: E=013.2236 | simE=013.2236 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m    1-Tot: E=010.7812 | simE=010.7795 | regE=000.0017 | optParE=000.0000 | relF=000.2073 | \u001b[0m\n",
      "\u001b[34m    1-Img: E=010.7812 | simE=010.7795 | regE=000.0017 |\u001b[0m\n",
      "\u001b[31m    2-Tot: E=008.2245 | simE=008.2192 | regE=000.0053 | optParE=000.0000 | relF=000.2772 | \u001b[0m\n",
      "\u001b[34m    2-Img: E=008.2245 | simE=008.2192 | regE=000.0053 |\u001b[0m\n",
      "\u001b[31m    3-Tot: E=005.4371 | simE=005.4287 | regE=000.0084 | optParE=000.0000 | relF=000.4330 | \u001b[0m\n",
      "\u001b[34m    3-Img: E=005.4371 | simE=005.4287 | regE=000.0084 |\u001b[0m\n",
      "\u001b[31m    4-Tot: E=002.8132 | simE=002.7993 | regE=000.0138 | optParE=000.0000 | relF=000.6881 | \u001b[0m\n",
      "\u001b[34m    4-Img: E=002.8132 | simE=002.7993 | regE=000.0138 |\u001b[0m\n",
      "\u001b[31m    5-Tot: E=003.3459 | simE=003.3275 | regE=000.0184 | optParE=000.0000 | relF=000.1226 | \u001b[0m\n",
      "\u001b[34m    5-Img: E=003.3459 | simE=003.3275 | regE=000.0184 |\u001b[0m\n",
      "\u001b[31m    6-Tot: E=004.0146 | simE=003.9933 | regE=000.0213 | optParE=000.0000 | relF=000.1333 | \u001b[0m\n",
      "\u001b[34m    6-Img: E=004.0146 | simE=003.9933 | regE=000.0213 |\u001b[0m\n",
      "\u001b[31m    7-Tot: E=004.1425 | simE=004.1206 | regE=000.0219 | optParE=000.0000 | relF=000.0249 | \u001b[0m\n",
      "\u001b[34m    7-Img: E=004.1425 | simE=004.1206 | regE=000.0219 |\u001b[0m\n",
      "\u001b[31m    8-Tot: E=004.0319 | simE=004.0107 | regE=000.0212 | optParE=000.0000 | relF=000.0220 | \u001b[0m\n",
      "\u001b[34m    8-Img: E=004.0319 | simE=004.0107 | regE=000.0212 |\u001b[0m\n",
      "\u001b[31m    9-Tot: E=003.2384 | simE=003.2193 | regE=000.0190 | optParE=000.0000 | relF=000.1872 | \u001b[0m\n",
      "\u001b[34m    9-Img: E=003.2384 | simE=003.2193 | regE=000.0190 |\u001b[0m\n",
      "\u001b[31m   10-Tot: E=002.7957 | simE=002.7797 | regE=000.0160 | optParE=000.0000 | relF=000.1166 | \u001b[0m\n",
      "\u001b[34m   10-Img: E=002.7957 | simE=002.7797 | regE=000.0160 |\u001b[0m\n",
      "\u001b[31m   11-Tot: E=002.2485 | simE=002.2353 | regE=000.0131 | optParE=000.0000 | relF=000.1685 | \u001b[0m\n",
      "\u001b[34m   11-Img: E=002.2485 | simE=002.2353 | regE=000.0131 |\u001b[0m\n",
      "\u001b[31m   12-Tot: E=001.7732 | simE=001.7612 | regE=000.0119 | optParE=000.0000 | relF=000.1714 | \u001b[0m\n",
      "\u001b[34m   12-Img: E=001.7732 | simE=001.7612 | regE=000.0119 |\u001b[0m\n",
      "\u001b[31m   13-Tot: E=002.1557 | simE=002.1453 | regE=000.0103 | optParE=000.0000 | relF=000.1212 | \u001b[0m\n",
      "\u001b[34m   13-Img: E=002.1557 | simE=002.1453 | regE=000.0103 |\u001b[0m\n",
      "\u001b[31m   14-Tot: E=002.4819 | simE=002.4723 | regE=000.0096 | optParE=000.0000 | relF=000.0937 | \u001b[0m\n",
      "\u001b[34m   14-Img: E=002.4819 | simE=002.4723 | regE=000.0096 |\u001b[0m\n",
      "\u001b[31m   15-Tot: E=002.1805 | simE=002.1700 | regE=000.0105 | optParE=000.0000 | relF=000.0948 | \u001b[0m\n",
      "\u001b[34m   15-Img: E=002.1805 | simE=002.1700 | regE=000.0105 |\u001b[0m\n",
      "\u001b[31m   16-Tot: E=001.6638 | simE=001.6511 | regE=000.0127 | optParE=000.0000 | relF=000.1940 | \u001b[0m\n",
      "\u001b[34m   16-Img: E=001.6638 | simE=001.6511 | regE=000.0127 |\u001b[0m\n",
      "\u001b[31m   17-Tot: E=001.5347 | simE=001.5233 | regE=000.0114 | optParE=000.0000 | relF=000.0509 | \u001b[0m\n",
      "\u001b[34m   17-Img: E=001.5347 | simE=001.5233 | regE=000.0114 |\u001b[0m\n",
      "\u001b[31m   18-Tot: E=001.9058 | simE=001.8941 | regE=000.0117 | optParE=000.0000 | relF=000.1277 | \u001b[0m\n",
      "\u001b[34m   18-Img: E=001.9058 | simE=001.8941 | regE=000.0117 |\u001b[0m\n",
      "\u001b[31m   19-Tot: E=002.7771 | simE=002.7654 | regE=000.0117 | optParE=000.0000 | relF=000.2307 | \u001b[0m\n",
      "\u001b[34m   19-Img: E=002.7771 | simE=002.7654 | regE=000.0117 |\u001b[0m\n",
      "\u001b[31m   20-Tot: E=001.5134 | simE=001.5007 | regE=000.0127 | optParE=000.0000 | relF=000.5028 | \u001b[0m\n",
      "\u001b[34m   20-Img: E=001.5134 | simE=001.5007 | regE=000.0127 |\u001b[0m\n",
      "\u001b[31m   21-Tot: E=002.0265 | simE=002.0142 | regE=000.0123 | optParE=000.0000 | relF=000.1695 | \u001b[0m\n",
      "\u001b[34m   21-Img: E=002.0265 | simE=002.0142 | regE=000.0123 |\u001b[0m\n",
      "\u001b[31m   22-Tot: E=001.7689 | simE=001.7538 | regE=000.0151 | optParE=000.0000 | relF=000.0931 | \u001b[0m\n",
      "\u001b[34m   22-Img: E=001.7689 | simE=001.7538 | regE=000.0151 |\u001b[0m\n",
      "\u001b[31m   23-Tot: E=002.0870 | simE=002.0694 | regE=000.0175 | optParE=000.0000 | relF=000.1030 | \u001b[0m\n",
      "\u001b[34m   23-Img: E=002.0870 | simE=002.0694 | regE=000.0175 |\u001b[0m\n",
      "\u001b[31m   24-Tot: E=002.4263 | simE=002.4088 | regE=000.0175 | optParE=000.0000 | relF=000.0990 | \u001b[0m\n",
      "\u001b[34m   24-Img: E=002.4263 | simE=002.4088 | regE=000.0175 |\u001b[0m\n",
      "\u001b[31m   25-Tot: E=001.3701 | simE=001.3545 | regE=000.0156 | optParE=000.0000 | relF=000.4456 | \u001b[0m\n",
      "\u001b[34m   25-Img: E=001.3701 | simE=001.3545 | regE=000.0156 |\u001b[0m\n",
      "\u001b[31m   26-Tot: E=001.8955 | simE=001.8820 | regE=000.0136 | optParE=000.0000 | relF=000.1815 | \u001b[0m\n",
      "\u001b[34m   26-Img: E=001.8955 | simE=001.8820 | regE=000.0136 |\u001b[0m\n",
      "\u001b[31m   27-Tot: E=001.6345 | simE=001.6214 | regE=000.0131 | optParE=000.0000 | relF=000.0991 | \u001b[0m\n",
      "\u001b[34m   27-Img: E=001.6345 | simE=001.6214 | regE=000.0131 |\u001b[0m\n",
      "\u001b[31m   28-Tot: E=001.0328 | simE=001.0197 | regE=000.0131 | optParE=000.0000 | relF=000.2960 | \u001b[0m\n",
      "\u001b[34m   28-Img: E=001.0328 | simE=001.0197 | regE=000.0131 |\u001b[0m\n",
      "\u001b[31m   29-Tot: E=000.7934 | simE=000.7789 | regE=000.0145 | optParE=000.0000 | relF=000.1335 | \u001b[0m\n",
      "\u001b[34m   29-Img: E=000.7934 | simE=000.7789 | regE=000.0145 |\u001b[0m\n",
      "\u001b[31m   30-Tot: E=000.8707 | simE=000.8570 | regE=000.0137 | optParE=000.0000 | relF=000.0413 | \u001b[0m\n",
      "\u001b[34m   30-Img: E=000.8707 | simE=000.8570 | regE=000.0137 |\u001b[0m\n",
      "\u001b[31m   31-Tot: E=001.0736 | simE=001.0591 | regE=000.0144 | optParE=000.0000 | relF=000.0978 | \u001b[0m\n",
      "\u001b[34m   31-Img: E=001.0736 | simE=001.0591 | regE=000.0144 |\u001b[0m\n",
      "\u001b[31m   32-Tot: E=002.2076 | simE=002.1934 | regE=000.0142 | optParE=000.0000 | relF=000.3535 | \u001b[0m\n",
      "\u001b[34m   32-Img: E=002.2076 | simE=002.1934 | regE=000.0142 |\u001b[0m\n",
      "\u001b[31m   33-Tot: E=001.0371 | simE=001.0226 | regE=000.0145 | optParE=000.0000 | relF=000.5746 | \u001b[0m\n",
      "\u001b[34m   33-Img: E=001.0371 | simE=001.0226 | regE=000.0145 |\u001b[0m\n",
      "\u001b[31m   34-Tot: E=001.8524 | simE=001.8380 | regE=000.0144 | optParE=000.0000 | relF=000.2858 | \u001b[0m\n",
      "\u001b[34m   34-Img: E=001.8524 | simE=001.8380 | regE=000.0144 |\u001b[0m\n",
      "\u001b[31m   35-Tot: E=001.0368 | simE=001.0220 | regE=000.0148 | optParE=000.0000 | relF=000.4004 | \u001b[0m\n",
      "\u001b[34m   35-Img: E=001.0368 | simE=001.0220 | regE=000.0148 |\u001b[0m\n",
      "\u001b[31m   36-Tot: E=001.3257 | simE=001.3123 | regE=000.0134 | optParE=000.0000 | relF=000.1242 | \u001b[0m\n",
      "\u001b[34m   36-Img: E=001.3257 | simE=001.3123 | regE=000.0134 |\u001b[0m\n",
      "\u001b[31m   37-Tot: E=001.0771 | simE=001.0624 | regE=000.0148 | optParE=000.0000 | relF=000.1197 | \u001b[0m\n",
      "\u001b[34m   37-Img: E=001.0771 | simE=001.0624 | regE=000.0148 |\u001b[0m\n",
      "\u001b[31m   38-Tot: E=001.3931 | simE=001.3785 | regE=000.0146 | optParE=000.0000 | relF=000.1320 | \u001b[0m\n",
      "\u001b[34m   38-Img: E=001.3931 | simE=001.3785 | regE=000.0146 |\u001b[0m\n",
      "\u001b[31m   39-Tot: E=001.1441 | simE=001.1300 | regE=000.0142 | optParE=000.0000 | relF=000.1161 | \u001b[0m\n",
      "\u001b[34m   39-Img: E=001.1441 | simE=001.1300 | regE=000.0142 |\u001b[0m\n",
      "Epoch    40: reducing learning rate of group 0 to 5.0000e-03.\n",
      "Epoch    40: reducing learning rate of group 1 to 5.0000e-03.\n",
      "\u001b[31m   40-Tot: E=001.4341 | simE=001.4203 | regE=000.0138 | optParE=000.0000 | relF=000.1191 | \u001b[0m\n",
      "\u001b[34m   40-Img: E=001.4341 | simE=001.4203 | regE=000.0138 |\u001b[0m\n",
      "\u001b[31m   41-Tot: E=001.3062 | simE=001.2907 | regE=000.0155 | optParE=000.0000 | relF=000.0555 | \u001b[0m\n",
      "\u001b[34m   41-Img: E=001.3062 | simE=001.2907 | regE=000.0155 |\u001b[0m\n",
      "\u001b[31m   42-Tot: E=000.9926 | simE=000.9770 | regE=000.0156 | optParE=000.0000 | relF=000.1574 | \u001b[0m\n",
      "\u001b[34m   42-Img: E=000.9926 | simE=000.9770 | regE=000.0156 |\u001b[0m\n",
      "\u001b[31m   43-Tot: E=000.6188 | simE=000.6039 | regE=000.0148 | optParE=000.0000 | relF=000.2310 | \u001b[0m\n",
      "\u001b[34m   43-Img: E=000.6188 | simE=000.6039 | regE=000.0148 |\u001b[0m\n",
      "\u001b[31m   44-Tot: E=000.6223 | simE=000.6080 | regE=000.0143 | optParE=000.0000 | relF=000.0022 | \u001b[0m\n",
      "\u001b[34m   44-Img: E=000.6223 | simE=000.6080 | regE=000.0143 |\u001b[0m\n",
      "\u001b[31m   45-Tot: E=000.5898 | simE=000.5753 | regE=000.0145 | optParE=000.0000 | relF=000.0204 | \u001b[0m\n",
      "\u001b[34m   45-Img: E=000.5898 | simE=000.5753 | regE=000.0145 |\u001b[0m\n",
      "\u001b[31m   46-Tot: E=000.5722 | simE=000.5575 | regE=000.0146 | optParE=000.0000 | relF=000.0112 | \u001b[0m\n",
      "\u001b[34m   46-Img: E=000.5722 | simE=000.5575 | regE=000.0146 |\u001b[0m\n",
      "\u001b[31m   47-Tot: E=000.5674 | simE=000.5528 | regE=000.0147 | optParE=000.0000 | relF=000.0030 | \u001b[0m\n",
      "\u001b[34m   47-Img: E=000.5674 | simE=000.5528 | regE=000.0147 |\u001b[0m\n",
      "\u001b[31m   48-Tot: E=000.5627 | simE=000.5480 | regE=000.0146 | optParE=000.0000 | relF=000.0031 | \u001b[0m\n",
      "\u001b[34m   48-Img: E=000.5627 | simE=000.5480 | regE=000.0146 |\u001b[0m\n",
      "\u001b[31m   49-Tot: E=000.5584 | simE=000.5438 | regE=000.0146 | optParE=000.0000 | relF=000.0027 | \u001b[0m\n",
      "\u001b[34m   49-Img: E=000.5584 | simE=000.5438 | regE=000.0146 |\u001b[0m\n",
      "\u001b[32m-->Elapsed time 14.31553[s]\u001b[0m\n",
      "Writing parameter file = test2d_tst.json\n",
      "Writing parameter file = test2d_tst_with_comments.json\n"
     ]
    }
   ],
   "source": [
    "si.register_images(I0_syn, I1_syn, spacing_syn, model_name='svf_scalar_momentum_map',\n",
    "                   nr_of_iterations=50,\n",
    "                   use_multi_scale=False,\n",
    "                   visualize_step=None,\n",
    "                   optimizer_name='sgd',\n",
    "                   learning_rate=0.01,\n",
    "                   rel_ftol=1e-7,\n",
    "                   json_config_out_filename=('test2d_tst.json', 'test2d_tst_with_comments.json'),\n",
    "                   params='test2d_tst.json',\n",
    "                   recording_step=1)\n",
    "h = si.get_history()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "plt.subplot(231); show_image(h['recording'][-1]['iS'], 'Source image')\n",
    "plt.subplot(232); show_image(h['recording'][-1]['iT'], 'Target image')\n",
    "plt.subplot(233); show_image(h['recording'][-1]['iW'], 'Warped image')\n",
    "plt.subplot(234); show_warped_with_grid(h['recording'][-1]['iW'],h['recording'][-1]['phiWarped'], 'Deformation (at convergence)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OQ6+DBw8yYsQILrvsMt59912mTp3qMbueeuop7r33Xq655hqnUwyMGjWKn3/+mcsuu4zt27cDMGHCBPLz82ttmHCu7k8++YT58+czdOhQBg0axIoVKxrsa9iwYdx0002MHDmSwYMHc88999T6wgCIj4/nscce46qrruKGG26ge/fuNabc6rPPWwRsPm34O7/y5nmrKH1yOrm9enP7uk88aKFv4o95pT2BP+oO1HzanuSHH35g5cqVfPxxzRvy5tRdXFxMSEgI5eXlTJw4kTvuuINrr722Qfu8RcDm04a/HyC179uNI4D+jDxykNT10FEOyFW3L8UQe5qnnnqKtWvXsnhx7ajg5tT95ptvsmnTJsxmM1deeSVjxoxp1D5v4Qtx2l6j8glrwnkJ7NdqCS7IJy8zj4jWgb1rtxw3OwX56vZmuGBL8+abb9Z6bdq0aezcuRNJkqq0T548mTvuuMNrdrzyyitO2+dtWjRO29sUFhYSERGBWqOhpE08rZKTOPnHUS4a3fhqK3+mUrfckKvuyphdufD2228DgT8tVB/ujrdP35fFxMRU/S5VRJCk/xn4ESTVdcsJueqWk8OujqLbNXzaaefm5lb9buzWEYCiI4EfQVJdt5yQq24lr7S8COh82tUjW6J7dwbAfjLwc5AoeYYVFBTqw6eddvXb5Q59uwNglEEEiVynCeSqW5kmkBcBPT2SkZFR9Xvbru2w6PQYi4vIPFN71VMgUV23nJCrbl9eCVo974qz3HzzzU6lJDhX93//+19KS//OiuFsO/6Gu+Pt0067eiIglUpFabt2AJzafaSlTGoWnMn0FYjIVbcnQx0lSarKntfcVPa9ZMmSqmRSDXGu7o8//hiT6e9tBZ1tpyVx5f12d7z96/4kMQFOniDzr+MwbkhLW6OgUIOZz67ySrtPvHbuxlE1OTd/9AMPPMAXX3yBxWIhMTGRDz/8kJCQEH755Reef/55IiMj6dOnD6dPn+brr7/mjTfeIDg4mH/9618AXHrppXz99dckJCRU9VFcXMydd95Jfn4+VquV5557jmuuuabO3NXXXnst69evZ/ny5XzxxReAI5wzISGB5cuXM3XqVPbs2YPJZGLcuHE888wzzJkzh/T0dMaNG0dUVBTLly+nT58+rF+/nqioKD766CMWLHBkgJ44cSIPPvhgVd8DBw7kt99+o02bNixYsKDezTR8JV/3jh07Ajfk79w8AkHdHREkJQEeQVJX/gQ5IFfdnuD48ePccsstLF26lPnz57Ns2TI2btxI3759+c9//kNZWRmPPfYYS5YsYeXKlWRnZzepfYPBwLx589i4cSPLly/nhRdeqHpwXNn3pk2bakyn3HvvvWzevJl169YRHx/PQw89BMDzzz/P+vXrWbduHdu2bePAgQNMnjyZuLg4li9fzvLly2v0vXfvXhYuXMgvv/zCmjVrmDdvXlVirJMnT3Lfffexfft2wsPDayWDqs5jjz3Gm2++yYYNG5g+fTrTpk2rKktPT2flypUsWrSI6dOnA7B+/XpOnjzJ2rVr2bx5M/v27WPbtm21NGs0GmbOnMmaNWtYunQpx445wpJDQ0MZPHgwa9Y4NvpaunQpY8eODewVkbGxsTX+bt27C/mAdDK5ZQxqJs7VLRf8XXdjV8T1Ybfb3f4gt2/fnosvvpjVq1dz5MgRRo8eDTh2uL/44os5duwYiYmJVbldbrzxRubOnet0+5Ik8eqrr7Jt2zZUKhVpaWlkZmbW6Ls+nnnmGYYOHcqoUY735/vvv2fu3LmUl5eTkZHB4cOH6dWrV73H79ixgzFjxlSlObj22mvZvn07o0ePpkOHDpx//vkAXHjhhSQn1+0biouL+e2337j33nurXqueanbMmDGoVCp69OhRlSlww4YNbNiwgcsvvxxw5Os+ceIE7dq1q6F59+7dDB48uGph2HXXXceJE47tESdOnMgHH3zAmDFjWLhwIbNmzXI7YZRPO+2srKwa39yJ/bqzFzCePeORE91XOVe3XJCr7vLycre3WqvMHy1JEldccQWfffZZjfKGUrZqNJoa4ZbVnVkl33zzDdnZ2WzYsAGtVkufPn2q6jWUW3vhwoWkpKRU5flOSkriww8/ZN26dQQFBfHYY4/V2V91GgoFrf6+Ve4KUxfV83U31s65+brvueeeGnWTk5OblK972rRpNfJ1WywWt8bbp73euWv0W3eIxWwwYjCVkpUSuBEkgZyLoiHkqtuT9O/fn507d3LypGMKsbS0lOPHj9O1a1dOnz5ddSW6bNmyqmMSEhLYt28fAPv27atz15TKrdm0Wi2//vorKSkpjdqyd+9ePvzwQ+bMmVN1gVVUVERQUBBhYWFkZWWxdu3aqvp15eIGxxz7ihUrKC0tpaSkhJ9//plBgwY14V0JrHzdPn2lHRkZWeNvlUpFaXxb9CePc3rvUWI7+PftdH2cq1suyFW3J6NHoqOj+eijj7j//vurrmCfe+45unTpwsyZM5kwYQKRkZH069ev6pixY8fy9ddfc9lll9G3b186d+5cq90JEyZw2223MWzYMHr37k3Xrl0bteXTTz8lPz+fcePGAY7pi9mzZ3PBBRcwaNAgOnToUGPX9rvvvpubb76Z2NjYGvPaffr04bbbbmPEiBGAY8rhggsuqHcqpD4++eQTpk6dyjvvvIPVamX8+PH07t273vrDhg3j6NGjjBw5EnBkoZwzZ06t8aqerzsuLq7OfN2vvfZaVb5ud8fbL/JpV2fRbc8SsWEj1kcmM/bZu9010Sfxx7zSnsAfdftTPu3KnNKSJDFt2jQ6depU9XCwJQikhFFNydcd0Pm06/owBHVNhA1QcvR0s9vTXLjrBPwVuepurpS08+bN4+uvv8ZisXDBBRfUmqttbgIpFW9T8nUHdJx2XYlVont2ogiwn258Ts1fURLpyIvmyrny0EMPteiV9bl4S3dlvu7q+FK+bnd1+7TTLikpITo6usZr7ft05SBgOBu4OUjq0i0H5Kq7pVYwtjTe0l2Zr9tXcVe3T0eP1LXRa9tu7bBqdQQVFZKbkdcCVnkfuW5wK1fd7sbt+iuKbtdo1GkLIf4nhMgUQuyv9lqkEOIXIcSxip9e2W4kPT291mtqtZqSNm0AOL3nqDe6bXHq0i0H/FG3EAKLxeJWG76cMMqbKLodi5+aGurqzPTIl8CHwLxqrz0NrJMk6Q0hxNMVfz/VpJ6doL5vJCmhHSQnkX7gBIwKvK3HlCsQ/6EytrisrMzlNkpLSxtcoBKoKLodX/pNTZTWqNOWJGmzECLxnJevA66o+H0usBEvOO36MnwZunSALVspOnLa0136BL6e2cxb+KNuIQShoaFutaFWq2W5E72i2zVcndOOlSQpDaDiZ2uXLWiA+pLaRJ7XCQDbqcDMQdLUZD6BgqJbXii6XcOr0SOZmZlMmjQJjUaDzWZj/PjxTJkyhfT0dIKDg1Gr1VXLY3Nzc5EkiZiYGDIyMggJCUEIQVJSErGxsWRlZSGEIDIyEl1sGCZAd+YMSUlJxMXFkZ6ejlarJTw8nOzsbMLDw7FYLJhMpqpynU5HaGgoOTk5REREYDKZKCsrqyo3GAwYjUby8vKIioqiqKgIi8VSVW40GtHpdBQUFBAdHU1BQQFWq7Wq3BlN4AjEP1dTVlYWYWFh2Gw2ysrKMJvNAaWppKSk0XEKCgoiKSkpoDQ5M05lZWVkZGQElCZnxkmSJPLz8wNKkzPjpFKpSEpKalBTQzi1IrJieuQnSZJ6V/x9BLhCkqQ0IUQbYKMkSd3PPc7dFZGZmZm0bl37It5qsbKm4zDUNhuDD60mNMK921Nfoz7dgY6iW14ouuunoRWRrk6PLAcq15DfDfzgYjsNUn0Xi+podVpKWjvyjpzad9wbXbco9ekOdBTd8kLR7RrOhPwtArYD3YUQZ4QQk4A3gKuEEMeAqyr+9jgNxe3a2ju2Hkv7K/CctlzjlRXd8kLR7RqNOm1Jkm6TJKmNJElaSZLaSZL0uSRJOZIkDZckqWvFz1y3rKiHhuJ2dV0ciYUKAzCCxB/jlT2BolteKLpdw6dXRDaUKDyihyOCxHKydu5ff8fdhPj+iqJbXii6XcOnnXZD8a9tznfk/NWeOdtc5jQb7sb9+iuKbnmh6HYNn3baOTk59ZYlXtAZuxCEZGViKnF9NZov0pDuQEbRLS8U3a7h0067cqPMujAGGymJjkElSZz660QzWuV9GtIdyCi65YWi2zV82mk3FhpjrYggSf0zsJy2EgolLxTd8sLrIX8tSWNJeHQdEwDIP3KqOcxpNtxJPuTPKLrlhaLbNXzaaTcWzxjWoyMAluOnm8Ga5kOJX5UXim554fU47ZaksXjGNr0dESTqlMCKIFHiV+WFolteBHSctsFgaLA88cKuAARnpGOxBE5C9cZ0ByqKbnmh6HYNn3baRqOxwfLQiFBKWkWisZWTdOB08xjVDDSmO1BRdMsLRbdr+LTTzstrfA9IS/u2AJz9M3BykDijOxBRdMsLRbdr+LTTjoqKarSOOtERQZJ76KS3zWk2nNEdiCi65YWi2zV82mkXFRU1WiesuyOCpOxE4OQgcUZ3IKLolheKbtfwaaftzC7XrXs5IkhUSSneNqfZcHd3b39F0S0vFN2u4dNO25l4xo59uwAQlJ5OebnN2yY1C0r8qrxQdMsLWcdpA0TERWEKCUVnMXP22JlmsMr7KPGr8kLRLS8COk7b2dCYsnaOCJKUAIkgUUKh5IWiW14EdMifs8nCVRURJNkHAiNxlJIcXl4ouuVFQG+CUFBQ4FS9kK6JAJiOB0YEibO6Aw1Ft7xQdLuGTzvt6Ohop+q1rshBIgVIBImzugMNRbe8UHS7hk87bWe/kTpU5CAJSk3Fbrd706RmQbkCkReKbnnRolfaQojHhBAHhBD7hRCLhBAezQBjtTqXBComIRazMQiDqZSM5AxPmtAiOKs70FB0ywtFt2u47LSFEG2BR4D+kiT1BtTArW5Zcw7OxjMKITBV1E35y/83RFDiV+WFoltetHSctgYwCiE0QBCQ6mZ7NWhKPKNUEfaXdeS0J01oEZT4VXmh6JYXLRanLUnSWWAmkAykAQWSJK1xy5pzCA4OdrquoaNjv8ji48meNKFFaIruQELRLS8U3a6hcfVAIUQEcB3QEcgHvhFC3ClJ0vzKOpmZmUyaNAmNRoPNZmP8+PFMmTKF9PR0goODUavVFBYWEhMTQ25uLpIkERMTQ0ZGBiEhIZSWlpKUlERsbCxZWVkIIYiMjCQrK4uwsDBsNhslJSXExcWhinPscGxNSiEpKYnw8HAsFgsmk4m4uDjS09PR6XSEhoaSk5NDREQEJpOJsrKyqnKDwYDRaCQvL4+oqCiKioqwWCxV5UajEZ1OR0FBAdHR0RQUFGC1WqvKndEEUFxc3KCmnJwcQkNDSU9PR6vVEh4eTnZ2tl9rqhynhjQZjUaSkpICSpMz45STk4PNZgsoTc6Mk91uR6PRBJQmZ8bJZDKRlJTUoKYGfa8kSa467QnAKEmSJlX8fRcwUJKkhyrrbN++XerRo4dL7QMkJSXRoUMHp+oe2voXSTdOpiCuDbfs/c7lPn2BpugOJBTd8kLRXT+7d+/+Y/jw4f3rKnNnTjsZGCiECBJCCGA4cMiN9moRExPjdN2E3o4UrSFZmVit5Z40o9lpiu5AQtEtLxTdruHOnPZO4FtgN/BXRVufuGXNOeTm5jpdNzg8hNLwVqhtNlKO+Pe8dlN0BxKKbnmh6HYNt6JHJEl6SZKkHpIk9ZYkaaIkSWa3rKndfpPqm+PjATi73793sXF1ysrfUXTLC0W3a/j0isim3kaoEhxhf7lH/DsHiXLbKC8U3fKixaZHmoOMjKatbjR2bA9A6Un/zkHSVN2BgqJbXii6XcOnnbYz4S/VierueCJrTznrDXOajabqDhQU3fJC0e0aPu20m0rb3p0A0KeltbAlCgoKCt7Bp512cXFxk+q37ZaATaUiJC+X4sJSL1nlfZqqO1BQdMsLRbdr+LTTjo2NbVJ9rV5LabRjkj/JjyNImqo7UFB0ywtFt2v4tNPOyspq8jHlFWF/6Qf9N9ufK7oDAUW3vFB0u4ZPO23HQsumoengCHMDi9IAACAASURBVPvLP+a/YX+u6A4EFN3yQtHtGj7ttCMjI5t8TEgXRwSJ+dQZT5vTbLiiOxBQdMsLRbdr+LTTduU2IqZHIgDijP+G/Sm3jfJC0S0vAnp6JCwsrMnHtD/fEfZnSE/322WyrugOBBTd8kLR7Ro+7bRtNluTj4lJiMWq02MsLSErLccLVnkfV3QHAopueaHodg2fdtolJSVNPkalUlFaEVKTst8/I0hc0R0IKLrlhaLbNXzaabu6Aaa9nSPsL+uQfzptZcNTeaHolhctvbGvV3F1A0x9omO/yMLj/hn2p2x4Ki8U3fKixTb2bQ60Wq1Lx4V1dYT9WU/7ZwSJq7r9HUW3vFB0u4ZPO+3w8HCXjos7z7H1mDo11ZPmNBuu6vZ3FN3yQtHtGj7ttLOzs106rkNF2F9wZgbl5f73hNpV3f6OolteKLpdw6edtqvfSKHRrTCFhKK1Wjl70v+utpUrEHmh6JYXAX2lbbFYXD7W3KYNAGf/8r9sf+7o9mcU3fJC0e0aPu20TSaT6we3d4T9ZR/2v7A/t3T7MYpueaHodg23nLYQopUQ4lshxGEhxCEhxCC3rDkHd+IZ/Xm/SCV+VV4ouuVFS8dpvw+skiSpB9AHOORmezVwJ54xopsj7M+W7H9hf0r8qrxQdMuLFovTFkKEAZcBnwNIkmSRJCnfLWvOQafTuXxsfC9H2J/OD/eLdEe3P6PolheKbtdw50q7E5AFfCGE2COE+EwIEeyWNecQGhrq8rHte3ZEEoLgnGxMpWYPWuV93NHtzyi65YWi2zU0bh7bD/iXJEk7hRDvA08DL1RWyMzMZNKkSWg0Gmw2G+PHj2fKlCmkp6cTHByMWq2msLCQmJgYcnNzkSSJmJgYMjIyCAkJISsrC6PRSGxsLFlZWQghiIyMJCsri7CwMGw2GyUlJcTFxZGeno5WqyU8PJzs7GzCw8MpiYgiJDeb4/uOERYfik6nIzQ0lJycHCIiIjCZTJSVlVUdbzAYMBqN5OXlERUVRVFRERaLparcaDSi0+koKCggOjqagoICrFZrVbkzmsCxsWdDmlJTU+nevXudmiwWCyaTqapPf9HU0DhVarLZbOTk5ASUJmfG6eTJk8TGxgaUJmfGqaSkhPj4+IDS5Mw4ZWdnYzQaG9TUEMLVnNNCiDhghyRJiRV/DwWeliRpTGWd7du3Sz169HCpfYDCwkK3cs8uumoyEX/9hf6tF7nyrlEut9PcuKvbX1F0ywtFd/3s3r37j+HDh/evq8zl6RFJktKBFCFE94qXhgMHXW2vLtwNjVEnOPaLzDt62gPWNB9KKJS8UHTLC3d1uzM9AvAvYIEQQgecBO51s70alJWVuXV8cOcERzt+Fvbnrm5/RdEtLxTdruGW05YkaS9Q5yW8J3A3njG6eyJ5ACn+tZRdiV+VF4puedHScdpexd14xna9HWF/hnT/CvtT4lflhaJbXgR0Pm2DweDW8W06t6VcoyGoqJDcrAIPWeV93NXtryi65YWi2zV82mkbjUa3jldpNJTGOPaLTN7vP4mj3NXtryi65YWi2zV82mnn5eW53YatYr/IjAP+47Q9odsfUXTLC0W3a/i0046KinK7DV1F4qgCPwr784Ruf0TRLS8U3a7h0067qKjI7TZa9XA8jLSe8p+wP0/o9kcU3fJC0e0aPu20PZEkPa6XY+sxzZkzbrfVXCjJ4eWFolteBPQmCJ6I40zs0xWA4MxMzGb/OEmU+FV5oeiWF0qcdiMEtwqhpFUEGls5yQeTPGCV91HiV+WFolteBHSctqdCgixtHREkZ/xkv0glFEpeKLrlRUCH/HkqSbom0ZGDJO+If+wXqSSHlxeKbnnRkpsgeJ2CAs+sYgzp6th6zHTcP6ZHPKXb31B0ywtFt2v4tNOOjo72SDutKyJIRIp/RJB4Sre/oeiWF4pu1/Bpp+2pb+IOfboAEJSais1u90ib3kS5ApEXim55EdBX2lar1SPtRLWPxWwwYigzkZ6c6ZE2vYmndPsbim55oeh2DZ922p6K4xRCYIpvA0DS3mMeadObKPGr8kLRLS+UOG1n6eDIQZJ9yPcjSJT4VXmh6JYXAR2nHRwc7LG2gjo5IkhKjvl+BIkndfsTim55oeh2DZ922mq12mNtRZ3nSBxlT/L9xFGe1O1PKLrlhaLbNXzaaRcWFnqsrXYXOCJI9Km+v1+kJ3X7E4pueaHodg2fdtoxMTEea6vteQnY1GpC8nLJz/Xtk8WTuv0JRbe8UHS7httOWwihFkLsEUL85G5b55Kbm+uxtjRaLSWtHVuPnf7TczlICsvKWfvnGb7fdJgTOaXYJcntNj2p259QdMsLRbdraDxgw6PAISDMA23VQPKAA6yOrX07SEslff8JuOJCl9tJLzKz47fjJK3YjGHnLtqdOobBZmNXZDQ/djsP3SV96ThiAP3Oa0tsaNPzDHhat7+g6JYXim7XcMtpCyHaAWOAGcDjbllSB56+fdJ3SoDffnN567Eff/mTE4tWELFnD7FpKXSveF0SApvRSKvcbFrt+BV2/Ir0gWBVm3bkXdiXCW9NoW10qNP9KLeN8kLRLS/c1e3ulfYs4EnAeY/UBDIyMujQoYPH2mvVPRELUH4qucnHbtlyCGnSI/So2HXCptejGXgRXa67gvYjh6AJD6HgzyOcWrOD9E274K+DxKamEJuawiqdmkkfT3O6L0/r9hcU3fJC0e0aLjttIcS1QKYkSX8IIa6oq05mZiaTJk1Co9Fgs9kYP348U6ZMIT09neDgYNRqNYWFhcTExJCbm4skScTExJCRkUFISAiSJJGUlERsbCxZWVkIIYiMjCQrK4uwsDBsNhslJSXExcWRnp6OVqslPDyc7OxswsPDsVgsmEymqnJju0gsgDrlDIWFhZhMJsrKyqrKDQYDRqORvLw8oqKiKCoqwmKxEBYZw5FnZ9LWYqHs/F50vOcaOo2+gqIyE1arFSnESMrZswQnxNJx8niibhtBZEgohxavJHf6R8Ss+JmNm4Zzfm/HQBUXFzeoyWQyYTabndKk0+kIDQ0lJyeHiIgIpzVVvSdGIzqdjoKCAqKjoykoKMBqtVaVOzNOzmhyZpz0ej1JSUkBpcmZcTKZTGRkZASUJmfGyWazkZ+fH1CanBkngKSkpAY1Neh7XZ1fEUK8DkwEygEDjjntpZIk3VlZZ/v27VKPHj1cah8gJyfHozs2lxYWs7nb1djUaoafXIdB79x88/y3vib63dlYgoO5asdijDGRTve57NqHMf6+mzNXj+S+eS85dYyndfsLim55oeiun927d/8xfPjw/nWVuRw9IknSM5IktZMkKRG4FVhf3WF7guLiYk82R1BYCCURkahtNpIOOLcy8ujJDIxzvgCg7VMPNslhAwyc/jAAcevWsXv3CaeO8bRuf0HRLS8U3a7h03HasbGxHm/TWrH12Nn9jTtQm11i41OzCC4pwtTzPPrdf0OT+2vTrwfmy4egsZWz+7VPnXpy7A3d/oCiW14oul3DI05bkqSNkiRd64m2qpOVleXpJlEnOhJH5R1uPHHUym9/JeHXTdjUaoZ88CxCCJf6HDL9IewqFfHbtrBjy8FG63tDtz+g6JYXim7X8OkrbVedZEOEdksEoOxEwxEk2fmlFM6YDYD+rgnE9Orscp9R3ROxjRqOym7nwOufNLoAxxu6/QFFt7xQdLuGTzvtyMimzR87Q+ueDucrkhveeuyHlz4hMiMVU2wsV7z4T7f7vfzlB7FpNLTbvYuNq/5osK43dPsDim55oeh2DZ922t64ferQx+G0g9Lq33ps547DxH63DIDeb01DYzS43W9YQhyq8WMAOP32J9js9V9tK7eN8kLRLS8CenokLMzjK+OJbNcaszEIQ5mJtNMZtcrLrDYOPv0OmnIrluGX03XkpR7r+/IX7seq1xN/cD9rvv213nre0O0PKLrlhaLbNXzaadtsNo+3KYSgrI1j67HkP4/XKJMkiQUvf0GbwwewBAUx4j3nVzE6Q1BMJIY7xgOQ+d5nmK116/OGbn9A0S0vFN2u4dNOu6SkxDsNV0SQZB+sme3v5++2EvflXAAS/v0YQa09P+d2+dP3Yg4OIfbUcZZ98nOddbym28dRdMsLRbdr+LTT9tbGn0GdEwAoPv73Apv9B1Moe3YGapsNMWEcF04c45W+dWEhRN5/GwDGt2bx07JtteooG57KC0W3vFA29nWBqPM6AWBPckSQ5Baa+OP+5wkpLMDUswdXvfuEV/qtZMi0u7BfMQS9uQzr4y+xds3uGuXKhqfyQtEtLwJ6Y1+tVuuVdiu3HjOcPYvNLvHdlDeJPXGMslatuGrBm6i0nkgzXj9CrWbUvBlYL7kIo6mEgoeeZcuWQ1Xl3tLt6yi65YWi2zV82mmHh4d7pd223dtjU2sIyc/jq+lzaf/LGmxqNX0+nUFIm+bJ8avSablm8UzM5/ciuLiQ1Pum8ccexxy7t3T7OopueaHodg2fdtrZ2dleaVet1VJSsf4/ds6nAERMe5COQ/t6pb967TDquWbpe5i6diEsP5ejd03lwKEzXtPt6yi65YWi2zV82ml785vY3r4tAEKSsI64gkGP3ua1vhpCGxrCNT98gCkhgYisDPbe/jh5hYEbClVul+pNmqVceckLRbdr+LTTtlTsEuMNgns4VkaWdOjA6E9ebNE8CPrIcK7+8SNMsXFEpZ1h7wPTKSguazF7vMWBPSdY2P9WFr8+v85yb463L6Polhfu6vZpp20ymbzW9vAn7iR4yj2MXPY+miD3l6m7S3BsFMOXf0hZWDhxJ47yzaPvemRnd1/i9xlzaJ2agvqrxZTbaqcQ8OZ4+zKKbnnhrm6fdtrejOMMiolg6Av/JCS+tdf6aCphHeLp/Z+XsQtBu59/4vt6Ft/4I4f/PEnMNkdMemheLrvX76lVR4nblReKbtfwaactxzjOLiMGYL97AgCqN2ax54/jjRzhH/z29lxUdjt2leOUO/7d2lp15DjeoOiWGwEdp63TObeHY6DR718TMF18EQZTKQceeIGcfM8t901OzmbetI/YtvFPj7XZGKeOpxK5YQMAIU88CIDm1621pkjkOt6Kbnnhrm6fdtqhoaEtbUKLEBYezugvX8UUFUV0ShLLHnqzwVSuzrJ17R62j/oHrb9aQO6dDzP/hfqTVnmSLW/PQ1NeTtHFFzPkkVswhYYRlpPN3k37atTz5ngfT87hP49+wC+rdjdeuZmR63mu6HYNn3baOTk5LW1Ci5CTk4MhKpx+n83AptbQbv1avp31rcvt2ewSi2cuJvfe/yM8N5uyVq3QlJcT/en/WDjmEU6eqp2i1lOkns0hbNUaAC588l5UGg22IYMAOHrOFIm3xnvzlkPsHDuZTosXkfvE9Gb5omoKcj7P5Yi7un3aaUdERLS0CS1Cpe4Ogy4g/InJABhnfcTOX/c3ua38ojK+vOtlwme+j9ZqxXzVlVy7eykxbz+PxWikzZ97+GPUP1i5eKNTmw43lY3vzEdvLqOwd2+6Dr0QgK43jQBAvWlrjY0oPD3edkli0f/WkDPxEaIyUgEIz87k12X15zJvCeR+nssNd3X7tNNWQoJg8P/dTtnlg9FZLJyc8iLJKc5/Sx85nMLykf+k7dpfsKnVhD7zL8bNexVNkIGLJl7DkLVzKenaldCCPKT/e44vHppJUYnn4sOzcwoxfv8TAD0fv6fq9d5XX0JZcAjh2Zn8ufVA1eueHO9SSzmfPDWH8OenYzSVUH7JRdjGO/aePrPgR4/14wmaojsrt5jFn62kpMzqRYuaB+Xz7RouO20hRHshxAYhxCEhxAEhxKNuWVIHZWWBt8DEGarrFkIw+rOXKYlvS0RmOhtun0ZeYeODvuGnHewf+09anzyOqVUE5y2azeBHb6uxiCiycztu2PA54u5bEED8smV8N+5RSk1mj+hY997XGEtLKOrchV6jB1W9rtZqKR88AIDD3/xS9bqnxvtsTjHzbn2OxHnzUNntGO66mTHLZjFw6l1IQhD9xy6SkjI90pcnaIruFY+/S/jzr/DjjC+9Zk9zoXy+XcOdK+1yYKokSecBA4EpQoiebllzDkocpwN9aDBXfvMeZWHhxB47zNKJz1PWwLzsT/N+oeihpwkuKqCk53lctWEunS6rO6+KSqNh5JuP0unLdygLCSHuwF8sfnim21MlhYWlqJd8D0CnR++qteK083jHFInYtLVqEZEnxnvXvtP8MuZBErf9SrlWS9u3n+OKt/4PoVYT1bkdxeefj6a8nO2f/eB2X57CWd2FhaWEb9oMgH3FWr9ffKV8vl3DZactSVKaJEm7K34vAg4Bbd2y5hyUOM6/iezcjn7z3saq09N253YWPDyz1odWkiS+mb0U8cx0dBYLlhFXcP2qjwlpE91on91HDqTHf1/BplLR5uef+e7979zS8MtH3xFSmE9Ru3b0u2lYrfILrrkUc1AwrTLS+Gu7Iy3tubrtksS86fP45J5XOHQktcH+7JLE4rlrSb7xAeJOn8AcEUH/pf/h/HM2s0i8axwA0o+rsdaxKrMlcPY837J4HQZTKQCRZ1PYu+2gN83yOsrn2zU8MqcthEgE+gI7PdFeJQZDyy8vbwnq050wsDcdZ7+EXaWi7Q8/sOiVuVVldkliwb+/JOT1d1DbbEg3jWPsvFdR65zP3dvjqgGEPO548Kl/5wO2rau9atEZSsss2OY7ol3aPngnQlX7NFPrtFgGXQLAoW8c0SXn6v72/e9o/Z+PSVi1ksOj72beq19RbC6v1VZ+qYU5D79H6NMvEVxciOX8Xly1YS5xF/eqVbffhBGUhYQSkZ7KtpW/uaTP0zh7nmd9txoAi9EIwKGFK7xmU3OgfL5dQ/3yyy+71YAQIgRYCTwnSdLe6mW7d+9++fbbb2fevHl8/vnn5Ofn069fP86ePUt5eTlms5nMzEx0Oh0ZGRkUFBSg1+s5e/YsNpsNs9lMXl4eBoOBtLQ0ioqK0Gg0pKamIkkSJSUlZGVlYTQaOXv2LKWlpahUKtLS0gAoLCwkOzu7qtxkMiGEIC0tDZVKRX5+Pjk5OVXlZrMZu91Oeno6Go2GnJwccnNzq8otFgtWq5WMjAy0Wi3Z2dk1yp3RZDKZyMrKalBTfn4+ISEhdWoyxEeSrjbAb3sx/LGH3/XhtOvejq8en038VwsRgOXOGxnxxiOkpqY2WVNQ1zYcP5JK0MnTZG3YiXVof/R6ldOazqam8sPrC4jZuZPimBj6vzaF0oryc8cpq7gEaf12SnPyCR11CUajkaysLEwmEzs37YfnXkNtt1MaH09Qbi7Bv/3OzuVbSGkdS1xcGBkZGRxPzmX9nc/Q4ddNCMB+81gufucxhFFf5zilpqdzNrUA3eFjnM4upsfYIXVqKi0tZf4bC9n132/JbhWKQS957dzLyMigvLy8wXE6fSod26yKNMLPP4Jl43bKM7JIuOcG0tNSGzz39h5Pxmq1YrOUNXruNefnqbzc8QXs7c+Tr/kIq9VKXl5eg5oKCgrSOnXq9EmdPteduUshhBb4CVgtSdK755Zv375d6tGjh8vtJyUl0aFDB5eP91ec0b162iykr5Zg0enI7Nefdju2IQlB2NMPM9jNNLNWUxk/DL+PkJMnyejekxtXfkRIkL7R4yzlNuZP/YD4xUsACHvlSS69//p665ebLazoPhpdmYl2P35BaGsDHTp04MSJNHaPuZ/Q/FzMV13JuHmvsnfuj6S8+iG64mLK1RrOjh1L5KX9YMa7hBbkYQkO5rzZL9J1zNBG7Uw/eIq9w+7AqtXR/7dltGlTOwTr+89WoH9hBkKSsOj0ZN16M+NfvJeIEM9fHToz3t/8+3+E/vczCvv0YcLKj/i+51iM+XkEf/oOQ8cOqve4E8dT2T3mfgo7JHL3yg/QqH0nYEz5fNfP7t27/xg+fHj/usrciR4RwOfAoboctieIioryRrM+jzO6r37rUczDLkdnsdBuxzbsKhWt33rWbYcNoDUaGL7obcpCw4g9cpAlD7/d6EOvYpOFeRNfJn7xEiQhCHnyoQYdNoBGr8M88GIADixeQ1RUFAXFZWy9+xlC83Mp6dyZMZ+8gBCCvveM4+qdS7CPHoHGVk6H75cR+uQLhBbkUdatK1dumOuUwwaI69mRovPOQ2u1sOXz5bXK/9h5BF59ByFJlCW0R2cx03beV/x0xT2s+nGnx+PZGxtvSZIwr1gHQNsJoxAqFWLEZQCcWrKywWO3zPic0II82v65h3WLN3jGYA+hfL5dw52v3cHARGCYEGJvxb9r3LLmHIqKijzZnN/gjG4hBNd+8W/K+vfDGhRE4pzXuMiDO8hHdGhDz49fwa5SEb9iBQtfX0CBqe7Y4KycYpZc/xjtNqzDptHQ5t0XGfL4nU710+GG4QDYNm6loLCQbye/RuvjRykLC2f41zPRGP++sjVEteKaL6Zz3qL3KYtvA4D6lusYt/YzQhPim6Sv7e2OB5LmH1bVSBGQnlXI4QdewFBmwjSgP9ftWETixzMoi4wk5kwy9n8+zid3Tef02bwm9dcQjY33vt8OE510CovewMW3Xg1An4mOmPPQbTsoLqk7RPPo4TNE/fL3qtPM2V/6zMNXUD7fruJO9MgWSZKEJEkXSJJ0YcU/jz4ZUZKkN4xar+O6Hz9gzJFV9Bx7mcft6Db8YkKnPgBA9Oz/sKrfeD6582V+mL+OtDxHFMOp05msuvZB4vftwWIMottX73LhbSOd7uPCsUOx6A1Enklm+XOf03bdWmxqDed/NoNW7WPrPKbDlRczbudiLv9jGVe9/xSqJjxsreTiO0diDgoiOiWJnRVpYs1WGz9PepmotDOUxsYyeu4MhEpFj+uvZMxvi1Hdcp2j/19W89vwiezyUPRGY+N9YJ5jgZJlyCB0IY6HkImX9KQ4Ph5jSTHbvttY53HbZnyGptxKSd8LKQsJIeb0CX6Z/0uddVsC5fPtGr4zwVUHShxn4wghvLp7/JDH70A/eSKW0FDC83JIWLsG/RMvsLPfOD4d+zg7xj1AzKkTmCIiuXj5x3S9ss5puHrRBRkoG+A4puNKx61+5HP/ovNl/Ro8TqXVYGxbt1N3Bq3RgHTVlQAc+8IRT77oxc9o+9sOrDo9l371FoZWfyf20YYEc/X7T3Hh8k8oTUx07On5iGd2GGpovM2WcnTrNgFwXsXVNTjG3TjaEUqZtnRNreMO7U8iev16JCG4ZMYjBN99MwB5H36Judw3cq8on2/X8GmnrcRxtjxCCK7894OMPbyCC777CNXtN2Jq0waDqZT2u3YQkZlOSfv2DFvzOW3O7+JSHwnXDa/63Xb9GAY9NMFT5jfIRQ/cBECrrVtZ8tlKYuZ+BUC716YRd0HXOo+Jv7gXY1Z/QmlUFFFnkln65Adu29HQeG/7cRuh+bmURkbR8+oBNcouvsfhxCP/+IOsrIIaZTtmfIrGVk7p4EG079eDKx6/g7LQMKJSkljz5Sq3bfYEvnSeNyc+EaftLYwV8ahywxd1C7Wa+MF9ufrdqdyw5zsGbFqI8ZFJcMv1jFnzKeH1TGU4Q78bLsfUtQulF1/EqNlPetDqhmnftztFXbqgLzMR/OIM1HY74tbx9L2z4Ucz+vAQes16HkkIYr//gQ0/bnfLjobGO2Wx4+5DO2pYrXj31l0TKOzaFa3VwvaFf19t/7XnBK03b0ISgkEvOeLutcFGwibdCkDhf+dhttSOd3cVm13i110nSMt1Lu/76bN5zLlrOqvvnsFn0/7L+l8PUWLxjav/5sDdz7dPO20lSbrvEtE9kcufncSo95/EEBHmVlu6IAM3/DqPYV+90qTFQJ4g9lbH1arabsfUuxdXzfw/p47rftUA7Dddh8puJ/OZN8nMcf3hUn3jnZNbRPhOx3q1i/4xrs46UeMc6QDyf/z7gePvr3+G2mbDdPkQ4s//+45h6CO3YgpvRdTZFFZ9Xv9Wdum5Jfy685hTOdxzCk18+o8ZlIydyKaht/P1nJ8xl9f9sFOSJH765ld2XnUPHdasIuHwftp99RWWCZNYOuA2Pp78Fj8u30lmsWdy3/gqAb0JQkFBQeOVAhBFd/Mx4O4xmFtFUBYTw9UL3kClcf75wMi3HqGkbVvCszP56ZG3XQ4FrE/31gVr0JvLKOrUifjeneusc8ndY7CpVEQfPEDSyTT27TpK7JZfsatUDH7pgRp1tUEGIu6/HYDSOV9hOudqW5IkVi7fyYbLJ1Jy3d18ft1U9vyVXK/de/ad5sdRk0lc5Yg/aJWTRauXZjBv9MNs3X60Rt2sAhOfTn4b1SNPE56bhSkhAd1j/8B6xRDKDQZi0s6Q+MP3aP/5GOsvmcDi/632+9wq9eHuee72isiGOHPmzMvR0Y3nvagPrVbrF1ednkbR3Xxo9Dq63HsD3e6b0OQ7BpVWQ0jfXqQvWUH4iRMcje1Atz51O9eGqE/3zmffJzgrk+B7b6XT4D51HqsPNrJ33R8YUtM4pQsj6dvVhCUnUzb8CvpPvrFW/fb9unNg7o+EZmdy2BjJeQMcOd5yi0zMn/ohoW/PIqjEcdcQnnqG3CU/sSWtlIRLehNkcNwF2SWJb79YQ8HDzxKRmY45vBW9Pn8da2IHinfvJyL1LKXf/cyG47nEXXI+e38/xu93PkH8rp0gBNo7bmT0gjeI7d+T3rePocuDt6G9sBc5khpLehbB+Xno129m/bEcOl15EUad9x60twTOnOdpaWn1rohUrrR9EEV386IJDkIT7No8Y8IlvdBPugMA06vvkpyc3eQ26tJ98mgq0Qf3Y1OrGXBPw/H3bcc7YrftS34gdvs2bCoVQyrmss9FbdAT9YDDXvNnCygts7B122F+vOqfJHz7jWNe/6axXLJuHpZLLkJvLqPdvK9YffldLF2wgZxiM58+9A4hz00nqKQYc5/zufrX+XQcWtykowAADXpJREFUMYBLn7yHYdsXU37lUDTlVjos/Y6tl91OwZ0P0zolibKoKLrPf5cR70xFpddV6VbpdSSOGszIOS8y9uBPBP3TEeOf+MP3fD/6IfbsT2nye+rLuHue+7TTtlr9P9G7Kyi6/YthL95HcdeuhBQWsPbh15q8n+e5uq02O7/OWYpKkijt15fQ1pENHn/xrVdh1ekJz8lCJUlYrx5OTNeEeusPfuBGSqOiaZWZzvxbniXn1geJPX2Cslat6PLlTEZ++AyRvbowbvkHjoVF0TFEpZ8laOpzrB54Cx2WLQXAcN/tjFvxH4zV7AuOb821i96kx8JZlLVrR3heDlqrBduIKxi9bSGdhv8dAVPXeAu1msumP0T3uW9jDg0j/thhTl5/P9/O8/9UtJW4e5779PSI0WhE04Q5xkBB0e1fCJWKqEv7krLwR1olJ7H5VAHxg/oQYnRuqqdSt6Xczorvt7Pl0deJX+d4sBg/9T7aXtBwKKVap+Wv7QfRJSVjU6sZPO8NgiLq3zxWpVGTq9Ji2bSd8LMpqG02rJcPZtSy2bQ+v2aoY3SPjnS9dzypJVbMfx4iuLgIc0gIPebMoN/km+rM4AjQqmNbut17AyWRUcTePpaBz0xCY6iZv6ah8Y7o3J72N13Nsa1/YjxzBs3ajaw/WUDXKy/CoFU3+H74Os6c5347PaLEccoLf9Yd2yORyKceAqDd0u/YOPQOvv7vj5Q6EVqXdDaNpYs28tXwf6J96Ana79+HTatFe9cE+t92tVP9X/jQLdhVKjS33kBUx8aX9A+87zpMnTthNQYR98bTXPv1W+gjw+usqzbqueLVhxm8cQFhj97HsA3z6DJ6cKN9qLQaLrx/PF3HXl5neWPjHRzfmrGrPsZwzy2oJInEpd+yZPxU0rL9e/m7u+e5W1n+GsPdLH/Z2dm4c6Xuryi6/ZdjK7aw//lZGFMdmzac7dGLxBceZtiwC6p277HZJc4WlHF8/2lSdx3A8u1PxJ90RFuU6/UYJ4zl0ifvxti6aYmFyktKUQcZa+0SVB92swXUqiZFzHiSpoz3sZ9/5dDDL6MzmUjv1JXBC2bStWOMly30Ds7obijLn0/fi6rV/n0b5CqKbv+l6zVD6HzVQHbOXkT2h1/S9vAByiZOYc6VI9B370TZ4ePoTiURlXYGQ5mJyo+u1Wgk5PYbuPLxO9FHtXKpb01wUJPqq/QtG6HUlPHuOmYoYe3+w/ZbHiPu5DF+u+FBiue+Td8+Hb1ooXdw9zz36emRwsLCljahRVB0+zcqrYZBUycy4rdvYNwox639ujW0+c/HdFy/lranjmEoM2EJC8Pc70K492ZG7v2eK2c87LLD9keaOt6xfbozbPVnmOLjiUpP5fjND7Nxwz4vWec93D3PfXp6pLS0lKCgpl09BAKK7sAiY89hdr31BUgSrc7vRvuLziOuXw/0FdMfgaq7MVzVbcrOZ9WN/4fxyFHKjEGo33yRsTd7Psult3BGt99Oj+Tm5sryZFZ0BxaxfXtw7aI36y0PVN2N4apuY3Qrxq74LyvueBrDjl2UP/4C/53v8G/Cbge75Pgp2ZG0OggPQ0SEoY0IRxfZCmNMBEGRYehDghz/Qo0YQ4Iw6jToNSpUKlALgUqAqtpPtUogAJXK8VplHWefIbiruxKfdtrevAvwZRTd8kLR3XQ0wUau/fYd1jwwA81Pq+n4W9OTdlkq/lXGoli1Wsq1OuxChaRSIQmBXaVCqvjbrhJIQvz9t1Ahqf7+GyEcx6kECBWSWsWdP80mKKTmwi13x9unnXZMjH8+HXYXRbe8UHS7hkqjYeSnL5K85iryUzJQqVUIlQqVpuKnWo25xERJVh6m7DzM2fmU5xVgyy+AomJEmRmV2fFPbbGgtVrReniBV10X4e7q9mmnnZGRIcuNPxXd8kLR7TpCCDqMvBR33z3JbsdmMmM3lSHZ7Ug2O5LN5vhpd/xO5e8V5dhs2G027OV27JIdu9WGzWZDskmO1+129IbaETru6vZppx0SEtLSJrQIim55oehueYRK5cg/42IOmqbgrm6fDvlTUFBQUKiJTzvt4uLiljahRVB0ywtFt7xwV7dbTlsIMUoIcUQIcVwI8bRbltRBbKzrW1j5M4pueaHolhfu6nbZaQsh1MBHwGigJ3CbEKKnW9acQ1ZWlieb8xsU3fJC0S0v3NXtzoPIS4DjkiSdBBBCfA1cBxx0yyIg42wBf/5+hpLiEg4HF1bFNUoS1X6XkOwVPyWpqqzOn3ZHHbtdqvG73f53ud0u/d3WOe1W9SedE2NZX7hlRZhPZdC9qOtvIRAVgfl//3T8brVa0emTHX9XtFdZVtXFOW1DtXIBwvEffxefW9/xflYKlCr+kxz/1dBcS16lUdQd0tQQf7dXu22z2Yxef7ae46q9/xUmVv1+ztg0Fgdb9d5Uvieqmu+XEKLWGNbZTj1F1c+ZCqnnGlADq8WKVlct0b+Hw7adaa7akDr+FqLWud5YO/W+U/UUVOquPA8rz0GnEDV+1BiMJp6SNXDlrW+ov9seGIhGU/PauKmLcc7FHafdFqi+pcQZYED1CpmZmUyaNAmNRoPNZmP8+PFMmTKF9PT0/2/v/F6sKMM4/vnurqFtLZ5dd92DWip4IUEaRBh2YRohJdVFQVDgRdBNgUER1U0UeNFN9QeU5EVFUrpJCCWbUXhhZSoqFhJ4JPZXcVY2JIrdfbqYd3bnrGd3dWeP55yZ5wNy5n3PO6/P9z3vPPPMuzPz0N7eTmtrK2NjY3R3d1MulzEzuru7ufjbZc6ciA/ecgoTm5lsJzednX/qbUCdcN1ZZHBggEmboLe3d8rvtbW1USqVrvF7w8PD13VnSRqnXe10UXGi6unp4fjx49c0St6jWCgUACoe67xr03ral3VQHi3T1dk1HQG1xNFdIjptmd6G6BHT5PdxRDVV3wItLdGjqvH+LVP9RPu1tEz3G0ewFX1eB8kopTIKjIYpii6qXClMwsDgAMXe4nQ90dVAPMTTkdz0f1A9Uq78Wa47ao71hsr5ovMbDW2S0VGy76GhIXp7e6vvE7cNdlVcrUxFztWuQCpJjl1FZB4PWxjLpM65+qnyDZVXPaqMYqsE30ODg/QWiySbzTmmacZ8lu4qIumkuNj+0Mls/cw5HLMUhwYHKRaL0/3OHKt5+qxmr81st4Cg9kZ2mS8yX1nsmPJbsd8rlUpT20m/l/SLpVJp1j7TOO0/gDWJ8mpgIEV/UxS62inc387oaMeUU88TbUt7KNxgktkscGsHufy9l7ZPUihUT0CQZfKqu6Mj3bGd5u6Rn4ANktZJugV4GjicypoZTExMLGZ3TYPrzheuO1+k1b1gp21m48CLwNfABeCAmZ1PZc0Mrl69upjdNQ2uO1+47nyRVneqx9jN7AhwJJUFczDb+mbWcd35wnXni7S6G/qJyGZO9JoG150vXHe+SKu7oZ12X19fvU2oC647X7jufJFWd0M77YMHD9bbhLrguvOF684XaXU3tNMeHx+vtwl1wXXnC9edL9Lqrmli3/7+/j+B2e8Sn4dyubyis7Pzr0U0qSlw3fnCdeeL69R9544dO6qmuKmp03Ycx3EWl4ZeHnEcx3EqcaftOI7TRDSk0651coVGQtI+SSOSziXqOiUdlXQxfGbqhRyS1kg6JumCpPOS9oT6rOteKulHSWeC7rdC/TpJJ4Luz8JrITKHpFZJpyR9Fcp50X1J0llJpyX9HOoWPNcbzmnfjOQKDcZHwM4Zda8B/Wa2AegP5SwxDrxsZhuBLcAL4TfOuu5/ge1mtgnYDOyUtAV4B3gv6B4FnqujjbVkD9ErL2LyohvgQTPbbGb3hvKC53rDOW0SyRXM7D8gTq6QSczse659afjjwP6wvR944qYaVWPMbNDMfgnbfxMdyKvIvm4zszhB4JLwz4DtwOehPnO6ASStBh4FPghlkQPdc7Dgud6ITrtacoVVdbKlXqw0s0GIHBzQU2d7aoaktcA9wAlyoDssEZwGRoCjwO/AlfACNsjufH8feBWYDOUu8qEbohPzN5JOSno+1C14rqd6YVSNmDe5gpMNJN0GfAG8ZGZjadMwNQNmNgFslrQcOARsrNbs5lpVWyTtAkbM7KSkbXF1laaZ0p1gq5kNSOoBjkr6NU1njRhp1yy5QhMxLKkIED5H6mzPoiNpCZHD/tjM4ud6M687xsyuAN8RrekvlxQHUFmc71uBxyRdIlru3E4UeWddNwBmNhA+R4hO1PeRYq43otOueXKFJuAwsDts7wa+rKMti05Yz/wQuGBm7ya+yrru7hBhI2kZ8BDRev4x4MnQLHO6zex1M1ttZmuJjudvzewZMq4bQFK7pNvjbeBh4Bwp5npDPhEp6RGiM3ErsM/M9tbZpJoh6VNgG7ACGAbeBPqAA8AdwGXgKTPLTIZjSQ8APwBnmV7jfINoXTvLuu8m+qNTK1HAdMDM3pa0nigC7QROAc+aWSYzO4flkVfMbFcedAeNh0KxDfjEzPZK6mKBc70hnbbjOI5TnUZcHnEcx3FmwZ224zhOE+FO23Ecp4lwp+04jtNEuNN2HMdpItxpO47jNBHutB3HcZoId9qO4zhNxP8ro3hdCGDWqAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "e_p, = plt.plot(h['energy'], label='energy')\n",
    "s_p, = plt.plot(h['similarity_energy'], label='similarity_energy')\n",
    "r_p, = plt.plot(h['regularization_energy'], label='regularization_energy')\n",
    "plt.legend(handles=[e_p,s_p,r_p])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LDDDM (shooting, scalar momentum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading parameter file = test2d_tst.json\n",
      "Overwriting key = use_map; category = root.model.deformation; value =  True -> True\n",
      "Overwriting key = type; category = root.model.registration_model; value =  svf_scalar_momentum_map -> lddmm_shooting_scalar_momentum_map\n",
      "Overwriting key = name; category = root.optimizer; value =  sgd -> sgd\n",
      "Overwriting key = nr_of_iterations; category = root.optimizer.single_scale; value =  50 -> 50\n",
      "Overwriting key = rel_ftol; category = root.optimizer.single_scale; value =  1e-07 -> 1e-07\n",
      "mapLowResFactor = 1: performing computations at original resolution.\n",
      "Overwriting key = use_map; category = root.model.deformation; value =  True -> True\n",
      "Overwriting key = map_low_res_factor; category = root.model.deformation; value =  1.0 -> 1.0\n",
      "Overwriting key = type; category = root.model.registration_model; value =  lddmm_shooting_scalar_momentum_map -> lddmm_shooting_scalar_momentum_map\n",
      "Overwriting key = type; category = root.model.registration_model; value =  lddmm_shooting_scalar_momentum_map -> lddmm_shooting_scalar_momentum_map\n",
      "Using map-based lddmm_shooting_scalar_momentum_map model\n",
      "works in mermaid iter mode\n",
      "LDDMMShootingScalarMomentumMapNet(\n",
      "  (integrator): ODEBlock()\n",
      ")\n",
      "Overwriting key = lr; category = root.optimizer.sgd.individual; value =  0.01 -> 0.01\n",
      "Overwriting key = lr; category = root.optimizer.sgd.shared; value =  0.01 -> 0.01\n",
      "Using ssd similarity measure\n",
      "\u001b[31m    0-Tot: E=013.2236 | simE=013.2236 | regE=000.0000 | optParE=000.0000 | relF=  n/a    | \u001b[0m\n",
      "\u001b[34m    0-Img: E=013.2236 | simE=013.2236 | regE=000.0000 |\u001b[0m\n",
      "\u001b[31m    1-Tot: E=010.3573 | simE=010.3556 | regE=000.0017 | optParE=000.0000 | relF=000.2524 | \u001b[0m\n",
      "\u001b[34m    1-Img: E=010.3573 | simE=010.3556 | regE=000.0017 |\u001b[0m\n",
      "\u001b[31m    2-Tot: E=009.1353 | simE=009.1295 | regE=000.0057 | optParE=000.0000 | relF=000.1206 | \u001b[0m\n",
      "\u001b[34m    2-Img: E=009.1353 | simE=009.1295 | regE=000.0057 |\u001b[0m\n",
      "\u001b[31m    3-Tot: E=006.2139 | simE=006.2074 | regE=000.0066 | optParE=000.0000 | relF=000.4050 | \u001b[0m\n",
      "\u001b[34m    3-Img: E=006.2139 | simE=006.2074 | regE=000.0066 |\u001b[0m\n",
      "\u001b[31m    4-Tot: E=001.7078 | simE=001.6987 | regE=000.0091 | optParE=000.0000 | relF=001.6641 | \u001b[0m\n",
      "\u001b[34m    4-Img: E=001.7078 | simE=001.6987 | regE=000.0091 |\u001b[0m\n",
      "\u001b[31m    5-Tot: E=001.8305 | simE=001.8202 | regE=000.0103 | optParE=000.0000 | relF=000.0433 | \u001b[0m\n",
      "\u001b[34m    5-Img: E=001.8305 | simE=001.8202 | regE=000.0103 |\u001b[0m\n",
      "\u001b[31m    6-Tot: E=002.3340 | simE=002.3250 | regE=000.0089 | optParE=000.0000 | relF=000.1510 | \u001b[0m\n",
      "\u001b[34m    6-Img: E=002.3340 | simE=002.3250 | regE=000.0089 |\u001b[0m\n",
      "\u001b[31m    7-Tot: E=001.5296 | simE=001.5201 | regE=000.0095 | optParE=000.0000 | relF=000.3180 | \u001b[0m\n",
      "\u001b[34m    7-Img: E=001.5296 | simE=001.5201 | regE=000.0095 |\u001b[0m\n",
      "\u001b[31m    8-Tot: E=002.2336 | simE=002.2227 | regE=000.0109 | optParE=000.0000 | relF=000.2177 | \u001b[0m\n",
      "\u001b[34m    8-Img: E=002.2336 | simE=002.2227 | regE=000.0109 |\u001b[0m\n",
      "\u001b[31m    9-Tot: E=003.1966 | simE=003.1855 | regE=000.0111 | optParE=000.0000 | relF=000.2295 | \u001b[0m\n",
      "\u001b[34m    9-Img: E=003.1966 | simE=003.1855 | regE=000.0111 |\u001b[0m\n",
      "\u001b[31m   10-Tot: E=002.8776 | simE=002.8691 | regE=000.0085 | optParE=000.0000 | relF=000.0823 | \u001b[0m\n",
      "\u001b[34m   10-Img: E=002.8776 | simE=002.8691 | regE=000.0085 |\u001b[0m\n",
      "\u001b[31m   11-Tot: E=002.5881 | simE=002.5772 | regE=000.0109 | optParE=000.0000 | relF=000.0807 | \u001b[0m\n",
      "\u001b[34m   11-Img: E=002.5881 | simE=002.5772 | regE=000.0109 |\u001b[0m\n",
      "\u001b[31m   12-Tot: E=001.8616 | simE=001.8496 | regE=000.0120 | optParE=000.0000 | relF=000.2539 | \u001b[0m\n",
      "\u001b[34m   12-Img: E=001.8616 | simE=001.8496 | regE=000.0120 |\u001b[0m\n",
      "\u001b[31m   13-Tot: E=002.3493 | simE=002.3403 | regE=000.0091 | optParE=000.0000 | relF=000.1456 | \u001b[0m\n",
      "\u001b[34m   13-Img: E=002.3493 | simE=002.3403 | regE=000.0091 |\u001b[0m\n",
      "\u001b[31m   14-Tot: E=002.9250 | simE=002.9165 | regE=000.0084 | optParE=000.0000 | relF=000.1467 | \u001b[0m\n",
      "\u001b[34m   14-Img: E=002.9250 | simE=002.9165 | regE=000.0084 |\u001b[0m\n",
      "\u001b[31m   15-Tot: E=002.2011 | simE=002.1908 | regE=000.0103 | optParE=000.0000 | relF=000.2261 | \u001b[0m\n",
      "\u001b[34m   15-Img: E=002.2011 | simE=002.1908 | regE=000.0103 |\u001b[0m\n",
      "\u001b[31m   16-Tot: E=002.8462 | simE=002.8358 | regE=000.0104 | optParE=000.0000 | relF=000.1677 | \u001b[0m\n",
      "\u001b[34m   16-Img: E=002.8462 | simE=002.8358 | regE=000.0104 |\u001b[0m\n",
      "\u001b[31m   17-Tot: E=001.2547 | simE=001.2452 | regE=000.0095 | optParE=000.0000 | relF=000.7059 | \u001b[0m\n",
      "\u001b[34m   17-Img: E=001.2547 | simE=001.2452 | regE=000.0095 |\u001b[0m\n",
      "\u001b[31m   18-Tot: E=002.7015 | simE=002.6897 | regE=000.0118 | optParE=000.0000 | relF=000.3909 | \u001b[0m\n",
      "\u001b[34m   18-Img: E=002.7015 | simE=002.6897 | regE=000.0118 |\u001b[0m\n",
      "\u001b[31m   19-Tot: E=001.2665 | simE=001.2574 | regE=000.0091 | optParE=000.0000 | relF=000.6332 | \u001b[0m\n",
      "\u001b[34m   19-Img: E=001.2665 | simE=001.2574 | regE=000.0091 |\u001b[0m\n",
      "\u001b[31m   20-Tot: E=002.4549 | simE=002.4433 | regE=000.0116 | optParE=000.0000 | relF=000.3440 | \u001b[0m\n",
      "\u001b[34m   20-Img: E=002.4549 | simE=002.4433 | regE=000.0116 |\u001b[0m\n",
      "\u001b[31m   21-Tot: E=003.4108 | simE=003.3974 | regE=000.0134 | optParE=000.0000 | relF=000.2167 | \u001b[0m\n",
      "\u001b[34m   21-Img: E=003.4108 | simE=003.3974 | regE=000.0134 |\u001b[0m\n",
      "\u001b[31m   22-Tot: E=001.9938 | simE=001.9813 | regE=000.0125 | optParE=000.0000 | relF=000.4733 | \u001b[0m\n",
      "\u001b[34m   22-Img: E=001.9938 | simE=001.9813 | regE=000.0125 |\u001b[0m\n",
      "\u001b[31m   23-Tot: E=001.8990 | simE=001.8893 | regE=000.0097 | optParE=000.0000 | relF=000.0327 | \u001b[0m\n",
      "\u001b[34m   23-Img: E=001.8990 | simE=001.8893 | regE=000.0097 |\u001b[0m\n",
      "\u001b[31m   24-Tot: E=001.5328 | simE=001.5235 | regE=000.0094 | optParE=000.0000 | relF=000.1446 | \u001b[0m\n",
      "\u001b[34m   24-Img: E=001.5328 | simE=001.5235 | regE=000.0094 |\u001b[0m\n",
      "\u001b[31m   25-Tot: E=002.7811 | simE=002.7739 | regE=000.0072 | optParE=000.0000 | relF=000.3301 | \u001b[0m\n",
      "\u001b[34m   25-Img: E=002.7811 | simE=002.7739 | regE=000.0072 |\u001b[0m\n",
      "\u001b[31m   26-Tot: E=003.6842 | simE=003.6760 | regE=000.0082 | optParE=000.0000 | relF=000.1928 | \u001b[0m\n",
      "\u001b[34m   26-Img: E=003.6842 | simE=003.6760 | regE=000.0082 |\u001b[0m\n",
      "\u001b[31m   27-Tot: E=001.5822 | simE=001.5727 | regE=000.0095 | optParE=000.0000 | relF=000.8141 | \u001b[0m\n",
      "\u001b[34m   27-Img: E=001.5822 | simE=001.5727 | regE=000.0095 |\u001b[0m\n",
      "Epoch    28: reducing learning rate of group 0 to 5.0000e-03.\n",
      "Epoch    28: reducing learning rate of group 1 to 5.0000e-03.\n",
      "\u001b[31m   28-Tot: E=002.6234 | simE=002.6115 | regE=000.0119 | optParE=000.0000 | relF=000.2874 | \u001b[0m\n",
      "\u001b[34m   28-Img: E=002.6234 | simE=002.6115 | regE=000.0119 |\u001b[0m\n",
      "\u001b[31m   29-Tot: E=002.4052 | simE=002.3925 | regE=000.0128 | optParE=000.0000 | relF=000.0641 | \u001b[0m\n",
      "\u001b[34m   29-Img: E=002.4052 | simE=002.3925 | regE=000.0128 |\u001b[0m\n",
      "\u001b[31m   30-Tot: E=001.5716 | simE=001.5600 | regE=000.0116 | optParE=000.0000 | relF=000.3242 | \u001b[0m\n",
      "\u001b[34m   30-Img: E=001.5716 | simE=001.5600 | regE=000.0116 |\u001b[0m\n",
      "\u001b[31m   31-Tot: E=001.9166 | simE=001.9070 | regE=000.0096 | optParE=000.0000 | relF=000.1183 | \u001b[0m\n",
      "\u001b[34m   31-Img: E=001.9166 | simE=001.9070 | regE=000.0096 |\u001b[0m\n",
      "\u001b[31m   32-Tot: E=001.5140 | simE=001.5045 | regE=000.0095 | optParE=000.0000 | relF=000.1601 | \u001b[0m\n",
      "\u001b[34m   32-Img: E=001.5140 | simE=001.5045 | regE=000.0095 |\u001b[0m\n",
      "\u001b[31m   33-Tot: E=001.5110 | simE=001.5019 | regE=000.0091 | optParE=000.0000 | relF=000.0012 | \u001b[0m\n",
      "\u001b[34m   33-Img: E=001.5110 | simE=001.5019 | regE=000.0091 |\u001b[0m\n",
      "\u001b[31m   34-Tot: E=001.2756 | simE=001.2663 | regE=000.0093 | optParE=000.0000 | relF=000.1035 | \u001b[0m\n",
      "\u001b[34m   34-Img: E=001.2756 | simE=001.2663 | regE=000.0093 |\u001b[0m\n",
      "\u001b[31m   35-Tot: E=001.0605 | simE=001.0508 | regE=000.0097 | optParE=000.0000 | relF=000.1044 | \u001b[0m\n",
      "\u001b[34m   35-Img: E=001.0605 | simE=001.0508 | regE=000.0097 |\u001b[0m\n",
      "\u001b[31m   36-Tot: E=000.7230 | simE=000.7134 | regE=000.0096 | optParE=000.0000 | relF=000.1959 | \u001b[0m\n",
      "\u001b[34m   36-Img: E=000.7230 | simE=000.7134 | regE=000.0096 |\u001b[0m\n",
      "\u001b[31m   37-Tot: E=000.8094 | simE=000.7985 | regE=000.0109 | optParE=000.0000 | relF=000.0477 | \u001b[0m\n",
      "\u001b[34m   37-Img: E=000.8094 | simE=000.7985 | regE=000.0109 |\u001b[0m\n",
      "\u001b[31m   38-Tot: E=000.5643 | simE=000.5538 | regE=000.0105 | optParE=000.0000 | relF=000.1567 | \u001b[0m\n",
      "\u001b[34m   38-Img: E=000.5643 | simE=000.5538 | regE=000.0105 |\u001b[0m\n",
      "\u001b[31m   39-Tot: E=000.5870 | simE=000.5768 | regE=000.0102 | optParE=000.0000 | relF=000.0143 | \u001b[0m\n",
      "\u001b[34m   39-Img: E=000.5870 | simE=000.5768 | regE=000.0102 |\u001b[0m\n",
      "\u001b[31m   40-Tot: E=000.5880 | simE=000.5777 | regE=000.0103 | optParE=000.0000 | relF=000.0007 | \u001b[0m\n",
      "\u001b[34m   40-Img: E=000.5880 | simE=000.5777 | regE=000.0103 |\u001b[0m\n",
      "\u001b[31m   41-Tot: E=000.7338 | simE=000.7234 | regE=000.0104 | optParE=000.0000 | relF=000.0841 | \u001b[0m\n",
      "\u001b[34m   41-Img: E=000.7338 | simE=000.7234 | regE=000.0104 |\u001b[0m\n",
      "\u001b[31m   42-Tot: E=001.0815 | simE=001.0713 | regE=000.0102 | optParE=000.0000 | relF=000.1670 | \u001b[0m\n",
      "\u001b[34m   42-Img: E=001.0815 | simE=001.0713 | regE=000.0102 |\u001b[0m\n",
      "\u001b[31m   43-Tot: E=001.0800 | simE=001.0701 | regE=000.0098 | optParE=000.0000 | relF=000.0007 | \u001b[0m\n",
      "\u001b[34m   43-Img: E=001.0800 | simE=001.0701 | regE=000.0098 |\u001b[0m\n",
      "\u001b[31m   44-Tot: E=000.9331 | simE=000.9237 | regE=000.0094 | optParE=000.0000 | relF=000.0760 | \u001b[0m\n",
      "\u001b[34m   44-Img: E=000.9331 | simE=000.9237 | regE=000.0094 |\u001b[0m\n",
      "\u001b[31m   45-Tot: E=000.9812 | simE=000.9700 | regE=000.0112 | optParE=000.0000 | relF=000.0243 | \u001b[0m\n",
      "\u001b[34m   45-Img: E=000.9812 | simE=000.9700 | regE=000.0112 |\u001b[0m\n",
      "\u001b[31m   46-Tot: E=000.5073 | simE=000.4966 | regE=000.0107 | optParE=000.0000 | relF=000.3144 | \u001b[0m\n",
      "\u001b[34m   46-Img: E=000.5073 | simE=000.4966 | regE=000.0107 |\u001b[0m\n",
      "\u001b[31m   47-Tot: E=000.4907 | simE=000.4808 | regE=000.0100 | optParE=000.0000 | relF=000.0111 | \u001b[0m\n",
      "\u001b[34m   47-Img: E=000.4907 | simE=000.4808 | regE=000.0100 |\u001b[0m\n",
      "\u001b[31m   48-Tot: E=000.4557 | simE=000.4455 | regE=000.0103 | optParE=000.0000 | relF=000.0241 | \u001b[0m\n",
      "\u001b[34m   48-Img: E=000.4557 | simE=000.4455 | regE=000.0103 |\u001b[0m\n",
      "\u001b[31m   49-Tot: E=000.4437 | simE=000.4332 | regE=000.0105 | optParE=000.0000 | relF=000.0083 | \u001b[0m\n",
      "\u001b[34m   49-Img: E=000.4437 | simE=000.4332 | regE=000.0105 |\u001b[0m\n",
      "\u001b[32m-->Elapsed time 40.96299[s]\u001b[0m\n",
      "Writing parameter file = test2d_tst.json\n",
      "Writing parameter file = test2d_tst_with_comments.json\n"
     ]
    }
   ],
   "source": [
    "si.register_images(I0_syn, I1_syn, spacing_syn, model_name='lddmm_shooting_scalar_momentum_map',\n",
    "                   nr_of_iterations=50,\n",
    "                   use_multi_scale=False,\n",
    "                   visualize_step=None,\n",
    "                   optimizer_name='sgd',\n",
    "                   learning_rate=0.01,\n",
    "                   rel_ftol=1e-7,\n",
    "                   json_config_out_filename=('test2d_tst.json', 'test2d_tst_with_comments.json'),\n",
    "                   params='test2d_tst.json',\n",
    "                   recording_step=1)\n",
    "h = si.get_history()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "plt.subplot(231); show_image(h['recording'][-1]['iS'], 'Source image')\n",
    "plt.subplot(232); show_image(h['recording'][-1]['iT'], 'Target image')\n",
    "plt.subplot(233); show_image(h['recording'][-1]['iW'], 'Warped image')\n",
    "plt.subplot(234); show_warped_with_grid(h['recording'][-1]['iW'],h['recording'][-1]['phiWarped'], 'Deformation (at convergence)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "e_p, = plt.plot(h['energy'], label='energy')\n",
    "s_p, = plt.plot(h['similarity_energy'], label='similarity_energy')\n",
    "r_p, = plt.plot(h['regularization_energy'], label='regularization_energy')\n",
    "plt.legend(handles=[e_p,s_p,r_p])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
